Electronic copy available at: http://ssrn.com/abstract=2153272

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Thediversityofhighfrequencytraders

BjörnHagströmer&LarsNordén

StockholmUniversitySchoolofBusiness

September27,2012

Abstract

The regulatory debate concerning high frequency trading (HFT) emphasizes theimportance of distinguishing different HFT strategies and their influence on marketquality.UsinguniquedatafromNASDAQOMXStockholm,wearethefirsttoempiricallyprovidesuchadistinctionforequitymarkets.ComparingthebehaviorofmarketmakingHFTs to opportunistic HFTs (arbitrage andmomentum HFT strategies), we find thatmarket makers constitute the lion share of HFT trading volume (63‐72%) and limitordertraffic(81‐86%).Furthermore,marketmakershavehigherorder‐to‐traderatios,lowerlatency,lowerinventory,andsupplyliquiditymoreoftenthanopportunisticHFTs.Inanaturalexperimentbasedonticksizechanges,wefindthatbothmarketmakingandopportunisticHFTstrategiesmitigateintradaypricevolatility.Thefindingsindicatethat,e.g., the financial transaction taxproposedby theEuropeanCommission,whichwouldrender most HFT strategies unprofitable, would primarily hit market makers andincreasemarketvolatility.

Keywords:High‐frequencytrading;Marketmaking;Marketquality;Liquidity;Volatility

JELcodes:G14,G18

Please send correspondence to BjörnHagströmer, School of Business, StockholmUniversity, S‐106 91Stockholm,Sweden.Phone:+468163030;E‐mail:bjh@fek.su.se.WewouldliketothankNASDAQOMXforprovidingthedata,andPetterDahlström,MattiasHammarquist,FrankHatheway,andBjörnHertzbergforusefuldiscussions.Remainingerrorsareourown.BothauthorsaregratefultotheJanWallanderandTomHedeliusfoundationandtheToreBrowaldhfoundationforresearchsupport.

Electronic copy available at: http://ssrn.com/abstract=2153272

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1 INTRODUCTION

Recentadvancesininformationtechnologyemployedinequitymarketsallowtradersto

process information and submit orders at lightning speed. Firmswho utilize the new

technology for intraday trading for their own accounts are generally called high

frequency traders (HFTs).1 With typical holding periods measured in seconds or

minutes, resulting in large trading volumes, HFTs are now major players in equity

markets. Since the arrival ofHFTshas coincidedwithmassively increased limit order

submissions and cancellations, high intraday price volatility (including flash crashes),

and fragmentation of volumes acrossmarketplaces,many voices havebeen raised for

HFTregulation.

In the regulatory debate, it is important to recognize that HFT constitutes several

different trading strategies. Both the International Organization of Securities

Commissions (IOSCO) and the US Securities and Exchange Commission (SEC) have

emphasizedthatdistinguishingsuchstrategiesispivotalintheregulatorydesign.

“HFTisnotasinglestrategybutitisratherasetoftechnologicalarrangementsand

toolsemployed inawidenumberof strategies,eachonehavingadifferentmarket

impactandhenceraisingdifferentregulatoryissues.”(IOSCO,2011,p.23)

“Indeed, any particular proprietary firmmay simultaneously be employingmany

differentstrategies,someofwhichgeneratealargenumberoftradesandsomethat

donot.Conceivably, someof these strategiesmaybenefitmarketqualityand long‐

terminvestorsandotherscouldbeharmful.”(SEC,2010,p.46)

1WeuseHFTasabbreviationforbothhighfrequencytraderandhighfrequencytrading.

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Intheacademicliterature,akeyempiricalissueistheidentificationofHFT.Sofar,HFTs

havebeenidentifiedeitherthroughclassificationsmadebytheexchangesordata‐driven

definitionsimposingpriorsonwhatHFTsdo.2Noneofthesemethodshasbeenableto

distinguishdifferentHFTstrategies.Thus,thecurrentempiricalliteraturetreatsHFTsas

a homogenous trader group, forming a gap between the regulatory and academic

discussion.Themaincontributionofthisarticle istobridgethatgapbycharacterizing

HFTsubgroupsandinvestigatingtheirrespectiveinfluenceonmarketquality.

Specifically,weuseadatasetthat includesallmessages(executions,submissions,and

cancellationsoflimitorders)attheNASDAQOMXStockholmequitymarket.Ourdataset

isuniqueinthatweareabletoassociateeachmessagewithatraderidentity,enabling

us to track down the strategies of different member firms. With the aid of in‐house

expertise at the NASDAQ OMX, we classify all member firms (about 100) into three

categories:HFTs,non‐HFTs,andhybrid firmsthatengageboth inHFTandtrading for

clients. Though our classification is similar to that used for US stocks by Brogaard

(2011a; 2011b; 2012) andHendershott andRiordan (2011b), thanks to our access to

traderidentities,weareabletotaketheHFTclassificationastepfurtherthanprevious

literature.Usingametricofhowoftenamemberhasalimitorderpostedattheinside

quotes, we distinguish HFT market makers from opportunistic HFTs, such as

arbitrageursanddirectionaltraders.

WebaseourinvestigationonthethirtyconstituentstocksoftheOMXS30index(alarge‐

capSwedishstock index),whichwefollowduringonemonthofhighmarketvolatility

(August,2011)andonemonthofrelativelycalmmarkets(February,2012).Wefindthat

2 Brogaard (2011a; 2011b; 2012) and Hendershott and Riordan (2011) utilize a HFT data set withclassificationsprovidedby theNASDAQ.Kirilenko et al. (2011)use a data‐drivendefinition, classifyingHFTasthe7%ofintermediarieswiththehighesttradingvolume.Menkveld(2011)observestheactivitiesofoneparticularHFTthatdominatestradinginDutchstocksatChi‐X.

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withinthegroupofHFTs,marketmakersrepresentaround71.5%ofthetradingvolume

inAugust,2011,and62.8%inFebruary,2012.Duringbothmonths,morethan80%of

theHFTlimitordersubmissionsoriginatefromthemarketmakers.Theimplicationof

our findings is thatanyregulatorypolicydirectedatHFTsasagroupwouldprimarily

affect market makers. Currently, the European Union (EU) is considering a financial

transactiontaxof0.1%onallstocktransactions.AccordingtoEUprojectionssuchatax

would render low‐marginHFTstrategiesunprofitable.3Asmarketmaking isgenerally

consideredtobegoodformarketquality(see,e.g.,JovanovicandMenkveld,2011),our

resultsindicatethatafinancialtransactiontaxwouldbenegativeforequitymarkets.

HFTactivityandmarketqualityaretwointimatelyrelatedconcepts.AsHFTshavetheir

competitive advantage in low‐margin trades, where they utilize their speed of

information processing and order submission, they require high trading volumes to

covertheirinvestmentsintechnology.Thus,HFTactivitytendstoconcentratetoliquid

stocks.InordertoestablishwhetherHFTisgoodorbadformarketquality,exogenous

eventsthatinfluenceHFTactivitybutnotmarketqualitydirectlyareneeded.Brogaard

(2012)usestheshort‐salebanof2008inUSequitymarketsasanexogenouseventthat

removed HFT activity. He finds that the removal of HFT activity caused increased

intradayvolatility.Studyingalgorithmic trading(AT),which isamoregeneralconcept

thanHFT,Hendershottetal.(2011)usetheautomationofquotesontheNewYorkStock

Exchange as exogenous events, and Boehmer et al. (2011) use the availability of co‐

location services in a cross‐country investigation.4 Both find that AT has a positive

3"AutomatedTradinginfinancialmarketscouldbeaffectedbyataxinducedincreaseintransactioncosts,sothatthesecostswoulderodethemarginalprofit.Thiswouldespeciallyholdforthebusinessmodelofhigh‐frequencytradingphysicallycloselylinkedtothetradingplatformsonwhichfinancialinstitutionsundertakenumeroushigh‐volumebutlowmargintransactions."(EuropeanCommission,2011,p.5)4Algorithmictrading(AT)isatermthatmayspanallsortsoftradingstrategiesthatcanbecomputerized,includingtradingservicesprovidedtoclients.

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influence on liquidity. Boehmer et al (2011), however, also observe that short‐term

volatilityisamplifiedbyAT.

WeuseticksizechangesasexogenousinstrumentsforHFTactivity.AtEuropeanstock

exchanges, the tick size (minimumprice increment) depends on the stock price level.

Forexample,whenthepriceofastock increases fromSEK49 toSEK51, the ticksize

increases fivefold fromSEK0.01 toSEK0.05 (SEK is theabbreviation for theSwedish

currencykrona).Wehypothesizethatanincreasedticksizemakesmarketmakingmore

profitableandotherstrategies,suchasarbitragetrading,morecostly.Thus,wepredict

that a tick size increase will increase market making HFTs’ activities and decrease

opportunisticHFTs’activities,whileaticksizedecreasewouldhavetheoppositeeffect.

Ourresultsconfirmthat this is indeedthecase.Wefindthat in theabsenceofmarket

making HFT, an exogenous increase in opportunistic HFT activity mitigates intraday

volatility. When both opportunistic HFTs and market making HFTs are active, they

respond inoppositeways to ticksizechanges.As thevolatilityeffect is thenreversed,

weconcludethatmarketmakersmitigatevolatilityaswell.

Based on our event study results, market making is a HFT activity that mitigates

volatility. Thus, as market making constitutes the majority of total HFT activity, the

proposed EU financial transaction tax is likely to increase volatility. In general, our

findingsimplythatpolicymakers,bothregulatorsandexchanges,shouldencourageHFT

marketmaking.OpportunisticHFTsasagroupmitigatesintradayvolatility,butthisisa

diverse group of strategies. Future research should disaggregate that group to

determinetheprevalenceofmaliciousstrategies.

ThispaperiscloselyrelatedtoBrogaard(2011b).HeinvestigatesHFTtradingactivityin

asetof120USstockstradingatNASDAQ.ThoughthecategorizationbetweenHFTsand

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othertradinggroupissimilarinthatpaper,theabilitytodistinguishdifferentstrategies

and form HFT subcategories among the HFTs is a distinct feature of our paper. Our

findingthatHFTsareindeedaheterogeneousgroupoftradersshowstheimportanceof

distinguishingHFTstrategies.

TheonlypreviouspaperonHFTthatcanobservethetradesofindividualfirmsis,toour

knowledge, Kirilenko et al. (2011). They investigate the trading and quoting

surrounding themarket turbulenceofMay6,2010,called the flashcrash.Theydefine

HFT as the 7%most active intermediaries in themarket and find that HFTs did not

causebutmayhaveamplifiedthevolatilityintheflashcrash.Theirdataspansonlyone

security, S&P 500 E‐mini futures, in three days of extraordinary volatility. Our

investigationcanbeseenasacomplementtotheirpaper,aswecoverthirtystocksover

twomonthsofdifferentvolatilitylevels.

Finally,ourpaperrelatestotheworkofJovanovicandMenkveld(2011).Theydevelopa

model of HFTmarketmaking and find that such traders contribute to social welfare

underallreasonableparametervalues.Inanempiricalapplication,theyshowthatone

HFTwhodominatedthetradingatChi‐Xin2007‐2008behavedasthemiddlemeninthe

model, with low net inventories, predominantly passive trades, and fast trading. The

HFTmarketmakersinvestigatedinthispaperarealignedtothesameproperties.

In the next section we present our empirical setting and data, as well as our HFT

categorizationmethodology. Next,we present estimates of variousmetrics frequently

associatedwithHFT.WealsorunpanelregressionsonHFTactivityandmarketquality

measures. In Section 4 we divide the HFT group into subcategories. Specifically, we

study how market makers differ from other HFTs. Furthermore, we investigate how

marketmakerandopportunistic traderbehaviordifferacrosssegmentsof stocks,and

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how market maker activity is related to market quality measures. In Section 5, we

presentoureventstudywhereweanalyzethecausaleffectsofHFTonmarketquality.

Section6offersadiscussionofpolicyimplicationsaswellasconcludingremarks.

2 INSTITUTIONALDETAIL,DATA,ANDHFTCLASSIFICATION

WestudystocktradingattheNASDAQOMXStockholmexchange(henceforthNOMX‐St).

NOMX‐St is an electronic limit order bookmarket that is open from9 am to 5.30pm

everyweekdayexceptSwedishbankholidays.IfthedaybeforeaSwedishbankholiday

is a weekday, trading at that day closes at 1 pm. Opening and closing prices are

determinedincallauctions.Duringtheintermediarycontinuoustradingsessiontraders

maypostlimitordersormarketorders.Limitordersareexecutedbytheorderofprice,

time,andvisibility.AfeatureofNOMX‐Stthatisdistincttomostotherstockexchangesis

that only large orders may be (partially or completely) hidden. Depending on the

averagedaily turnover of a stock, orders have to beworth at least EUR50000 to be

eligible for non‐visibility (for stocks with average daily turnover exceeding EUR one

million,includingallstocksinoursample,theordersizethresholdforhiddenliquidityis

EUR 250 000, and for some of the stocks the threshold is even higher). Accordingly,

hidden orders constitute no more than 0.7% of all limit orders in our sample.5 All

messages are entered through the INET trading system,whichhas capacity to handle

more than a million messages per second at less than 0.25 milliseconds average

processing time. To cut latency (order processing time) further, NOMX‐St offers co‐

5Asapointofcomparison,Bessembinderetal.(2009)reportthatinasampleof100stocksatEuronextParisinApril2003,44%oftheordervolumeishidden.Inasampleof99NASDAQstocksinOctober2010,Hautsch(2012)findsthat14.6%ofalltradingvolumeisexecutedagainsthiddenliquidity.Healsoreportsthat hidden liquidity is associated with “enormous order activities” related to liquidity‐detectionstrategies (p.2). The lack of hidden liquidity at NOMX‐St is likely to induce less liquidity‐detectionstrategies,whichiningeneralshouldleadtolowerorder‐to‐traderatios.

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locationservices,whereclientscanpayapremiumtoplacetheirserversatthepremises

oftheexchange.

NOMX‐Sthasroughlyonehundredmemberfirmsthathavetherighttosubmitordersto

tradeatthestockmarket.Eachmemberfirmmaysell theserviceof tradingtoclients.

Theprovisionof tradingservices fromexchangemembers to tradersmaybedonevia

traditionalbrokers, throughdirectmarketaccess (DMA),or throughsponsoredaccess

(SA). DMA gives customers access to the market through the infrastructure of the

member firm. A typical example is retail investors who get DMA through internet

brokers.InthecaseofSA,thecustomerusesitsowninfrastructurebuttradeunderthe

member identity (MPID)of thesponsor.SA is increasinglypopularamongalgorithmic

tradingfirms,inparticularHFTs,asitallowsforlowerlatency(orderprocessingtime)

thanDMA.

2.1 Data,sampleselection,andsummarystatistics

We access allmessages that are entered into INET. Data used in this paper span the

messagesofstocksincludedinOMXS30,aSwedishstockindexincludingthethirtymost

traded stocks at NOMX‐St. Our limitation to the OMXS 30 constituents is due to that

HFTs tend to concentrate their activity to themost traded stocks.NOMX‐St hosts 55‐

65% of the trading volume in OMXS 30 stocks. The largest competitor is BATS Chi‐X

Europe(25‐30%),followedbyBurgundyandTurquoise(lessthan5%each).6Thetime

period studied includes August 2011 and February 2012. August 2011 was a highly

volatile month where HFT attracted extensive media attention.7 Being a much less

volatile month, February 2012 is included for comparison. As a final limitation, only

6Marketsharedataistakenfromhttp://www.batstrading.co.uk/market_data/venue/index/OMXS/7Reasons for theAugust,2011,volatility includeworriesaboutcredit ratingdowngradesof theUnitedStatesandFrance,aswellasconcernsaboutthesovereigndebtcrisisspreadingtoItalyandSpain.

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messagesfromthecontinuoustradingsessionareincluded,representingroughly97%

ofalltradesand99%ofalllimitordersubmissions.

The data includes all information contained in themessages entered into INET.Most

importantly,except forprice,volume, timeanddisplayproperties,all limitordersand

executionsareassociatedwithMPIDsaswellasuseridentificationnumbers(USERID).

The former identifies the firm that is a member of the exchange through which the

messageisbeingentered.Thelatteridentifieswho(whatbroker,trader,orclient)atthe

member firm is responsible for the message. Messages are time‐stamped to the

nanosecond(10‐9second).

Table1presentssummarystatistics.8Thefirstthreerowsincludemeans,medians,and

standard deviations estimated for August, 2011, and themiddle three rows hold the

samestatisticsforFebruary,2012.Thebottomthreerowscontainp‐valuesoftestsfor

differences,whereeachnullhypothesisstatesthatthereisnodifferenceineachstatistic

betweenthetwomonths.

Theaveragemarket capitalizationof stocks inoursample is SEK79billion inAugust,

2011,andSEK91billioninFebruary,2012;correspondingtoUSD12billionandUSD14

billionatanexchangerateofSEK/USD6.6.Thus, thestocksanalyzedhereareslightly

smalleronaveragethanthoseanalyzedintheHFTstudiesbyBrogaard(2011a,b;2012)

andHendershott andRiordan (2011b), averagingUSD18billion.The average trading

activity is slightly higher in our sample, 4962‐7522 executions per stock and day on

average, as compared to3090 in their sample.The average relativebid‐ask spread in

our sample (0.09‐0.10%) lies in the range between their medium‐cap and large‐cap

stocks(0.05‐0.13%,seeHendershottandRiordan,2011b).

8SummarystatisticsforindividualstocksaregivenintheAppendix.

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It isclear fromTable1 thatour twosamplemonthsdiffersubstantially.Realizedone‐

minute volatility is 0.215 in August, 2011, but only 0.044 in February, 2012 (the

differencebeing statistically significant both in termsofmeansandmedians).August,

2011,alsorecordssignificantlylessliquidityintermsofdepth,nominalbid‐askspreads,

aswellasrelativebid‐askspreads(theliquiditymeasuresarebasedonlimitorderbook

snapshots taken everyminute in each stock). The number of trades is 52%higher in

August,2011,thaninFebruary,2012,andthenumberoflimitordersubmissionis318%

higher.Giventhe largedifferences,wereportresults for thetwomonthsseparately in

oursubsequentanalysis.

ItisnotablefromTable1thatthenumberofcancellationsishigherthanthenumberof

limitordersubmissions.Thisisduetothatthevolumeoflimitordersisoftenpartially

cancelled.

2.2 HFTandnon‐HFTtraderclassification

Theuseofalgorithmsintradingisnowadayswidespread.Inasurveyconductedbythe

SwedishFinancialSupervisoryAuthority,20(7banksand13institutionalinvestors)out

of the 24 financial firms that participated claimed that they use algorithms in their

trading(Finansinspektionen,2012).InordertoinvestigatethenatureandimpactofAT

itisthusnecessarytobreakdownthetermintosubcategories.However,categorization

of traders is in general complicated by the fact that traders do not stick to any one

strategy. On the contrary, traders adapt and change their strategies in accordance to

their expected returns and risk taking.An important distinction, however,whichmay

also be observed in the data, is whether traders apply their strategies to their own

holdings or as services to clients. Applying this distinction to algorithmic trading,we

havethetwosubgroupsofAT,agencyalgorithmsandproprietaryalgorithms,wherethe

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latteriswhatistypicallyreferredtoasHFT.Agencyalgorithmfirmsprovideexecution

services for clients, typically using their infrastructure and market knowledge to

minimize price impacts of trading. HFT strategies, on the other hand,may be further

subdivided into market making and opportunistic trading, such as arbitrage and

directional(momentum)trading.9

InordertoanalyzethebehaviorofHFTsatNOMX‐St,weclassifymarketmemberfirms

into three categories: (1)memberswho are primarilyHFT, i.e., engage in proprietary

trading only and who use algorithms in their trading strategies; (2) members who

primarilytradeforclients;and(3)memberswhoengageinbothproprietaryandclient

trading. The categorization is done with the aid of NASDAQ OMX in‐house expertise

aboutmemberactivities.

Including all MPIDs, we classify 29members as HFT firms, 49members as non‐HFT

firms, and22members as hybrid firmswith both proprietary and agency activities.10

Our data set does not allow us to isolate the activities of HFTs accessing themarket

throughSA.Thus,suchactivityendsupamongthehybridfirms.

ToconfirmthattheHFTfirmsareindeedusingalgorithmsintheirproprietarytrading

we filter their activity with respect to USERID.Messages originating from algorithms

haveUSERIDsstartingwitheitherPRT(programtrading)orAUTD(automatedtrading).

IntheHFTgroup,98.2%ofallmessages(96.7%ofthetradingvolume)originatefrom

algorithmicUSERIDs.ThehighpropensityofsuchUSERIDsistakenasaproofofvalidity

of thequalitative categorizationprocess. In thenon‐HFTgroupand thehybrid group,

9SeeSEC(2010)andGomberetal.(2011)fordefinitionsofanddetailsaboutthesestrategies.10Confidentialityrequirementsdonotallowustodisclosethecategorization,butthecompletememberlistisavailablepubliclyonline:http://nordic.nasdaqomxtrader.com/membershipservices/membershiplist/

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13.5% and 63.6% of the messages (14.0% and 53.0% of the trading volume),

respectively, originate from algorithmic USERIDs, confirming that the use of AT is

widespreadinthemarket.

Our HFT categorization is similar to the procedure applied to a data set of NASDAQ

stocksutilizedbyBrogaard(2011a,b;2012)andHendershottandRiordan(2011b).The

HFT categorization used for the US data set is richer in the sense that it is dynamic

(continuously updated) whereas ours is done at one point in time (May 2012). One

advantageof our categorization relative to theUSdata set is that alongwith theHFT

group,wealsoidentifyagroupthatisfreefromHFTactivities.Thisfeatureallowsusto

benchmarkourHFTresultstoagroupofnon‐HFTmembers.Furthermore,whereasthe

USdatasetcontainsquoteinformationatBBOpricesonly(theinsidequotes),weaccess

order information at all levels in the order book. Having full information of the limit

orderbookallowsaricheranalysisofordersubmissionstrategies,thoughthepictureis

incomplete in the sense that we do not observe activity at other exchanges. The key

advantage of our data set, however, is that we are able to observe the activity of

individualHFTmembers,whereastheUSdatasetonlyindicateswhetheragiventrade

orquoteisassociatedwithone(notwhich)outof26HFTfirms.

To our knowledge, the only previous analysis of AT and HFT with access to trader

identities isKirilenkoetal. (2011),whostudy tradingbehaviorbeforeandduring the

flash crash on May 6, 2010. Their data contains trading records from three days of

extraordinary volatility in one asset (S&P 500 E‐mini stock index futures).We take a

broaderapproach, studyingboth tradesandquotes, in30different stocks,during two

monthswithbothvolatileandlessvolatiledays.

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Kirilenko et al. (2011) classify traders with small deviations from their target

inventories as intermediaries, and the 7% most actively trading intermediaries are

labeledasHFTs.Wethinkthataqualitativecategorizationtechniqueispreferabletoa

data‐drivenapproach,asitallowsustostudyHFTbehaviorwithoutimposingaprioron

whattheydo.Inthenextsection,weemployvariousmetricsoftradingbehaviortosee

whethertheHFTsinourdatasetconformtocommonconceptionsaboutsuchtraders.

3 CHARACTERISTICSOFHIGHFREQUENCYTRADERS

GiventheplethoraofHFTdefinitionsinthecurrentliterature(seeGomberetal.,2011)

it is interesting to study severaldimensionsofHFTactivity.According to SEC (2010),

HFTstendto(i)endthedaywithclosetozero inventories; (ii) frequentlysubmitand

cancellimitorders;(iii)usecolocationfacilitiesandhighlyefficientalgorithmsallowing

themtominimizedifferenttypesof latencies;and(iv)haveshortholdingperiods.The

aimofouranalysis inthissection istoseewhethersuchconceptionsofHFTbehavior

applytooursampleofHFTmembers.

Alongwithmeasuresoftradingandquotingvolumes,wereportvariousmetricsforthe

HFT sample aswell as the control groupof non‐HFTmembers.The groupof residual

(hybrid)membersislikelytocontainsubstantialHFTactivity,enteredthrough,e.g.,SA

ortradingdesksofbanksthatalsohaveclients.Asthisgroup isamixtureofHFTand

non‐HFTtrading,andanyresultsrelatedtotheiractivitywouldbedifficulttointerpret,

wedonotexplicitlyanalyzeitsactivities.

3.1 Metricsoftradingactivity

OurfirstmetricofHFTbehavioristheabsoluteday‐endinventory(thesumofallsigned

tradingvolumes)dividedbydaily tradingvolume,denoted |Inventory|.Marketmakers

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andopportunistictradersalikestrivetohavelow|Inventory|aspositionsheldovernight

aresubjecttoclearingandcapitalcosts.However,asweareunabletoobservetradesat

other exchanges, this metric may falsely indicate high inventory of firms that utilize

centralclearingfortradesatseveralexchanges(e.g.,cross‐marketarbitragestrategies)

orseveralassetclasses.Hence,high|Inventory|ofHFTsmaybeseenasanindicationof

arbitrageactivities.

AsecondfeatureoftenassociatedwithHFTisintensesubmissionsandcancellationsof

limit orders. As the algorithms continuously scan the markets for news about

fundamentals, order flows, and related prices, the optimal quotes are subject to

continuous changes. Intense order submissions and cancellations force other market

participants to store and analyze huge amounts of data. Thus, HFTs are sometimes

claimed to carry a negative externality. Accordingly, many exchanges apply fines to

marketparticipantswhohaveexcessivelimitordersubmissionsrelativetoexecutions.

Themost commonmetric for this,which is also applied byNOMX‐St, is the order‐to‐

traderatio,q/t,definedas thenumberofquotes (limitorder submissions)dividedby

thenumberoftrades(executions)duringthecontinuoustradingsessionforagivenday

and stock.11 Hendershott et al. (2011) use a relatedmetric, the number of messages

(limit orders submissions and cancellations) per $100 trading volume as a proxy for

market‐wide AT activity. Boehmer et al. (2011) use two other variations of quoting

intensities to approximate the amount of AT. The European Commission (2010) has

considered imposing limits on order‐to‐trade ratios in its review of the Markets in

11AtNOMX‐Stthelimitforfinesisq/t=250,measuredatamonthlyfrequency.

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Financial Instruments Directive (MiFID). German legislators are considering similar

limits.12

Adimensionoftradingactivitythatis,atleastsemantically,closelyrelatedtoHFTisthe

speedoftrading.Millionsofdollarsareinvestedintradingsysteminfrastructuretocut

the latency of information and order submissions. It is alsowell‐known that financial

firmspayrentstobeabletoco‐locatetheircomputersattheexchanges.AtNOMX‐Stthe

currenttradingsystem, INET,was introduced inFebruary,2010, to increasetheorder

processingcapacityandtoallowco‐locationofdataservers.HasbrouckandSaar(2011)

arguethat low‐latencytradingstrategiesisahallmarkofHFTs,andshowthatHFTsat

NASDAQintheUnitedStatesareabletorespondtonewseventswithin2‐3milliseconds

(in a sample from2007‐2008). Several theoreticalmodels use latency as the defining

characteristic of AT and HFT, finding that fast traders profit at the expense of slow

traders(JarrowandProtter,2011;McInishandUpson,2011).AccordingtoBiaisetal.

(2011)suchpotentialprofitsleadHFTstooverinvestmentintechnology.

To see whether the members included in our HFT sample are faster than other

members,wemeasurethelifetimeofeachlimitorderinoursample,fromsubmissionto

the first cancellation (limit order volume can be partially cancelled). Ourmeasure of

Minimum latency is theminimum limitorder lifetime for eachmember, eachday, and

eachstock.Furthermore,weusethe limitorder lifetimetocalculateaveragesforeach

member,stock,andday,referredtoasLimitorderduration.

DeviatingfromtheHFTfeaturesreportedbySEC(2010),wealsoreportthefractionof

trades where a member is on the passive side, i.e., where his limit order is hit by a

12http://www.thetradenews.com/news/Regions/Europe/Germany_seeks_to_pre‐empt_MiFID_II_with_new_HFT_law.aspx

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market order.Passive tradesdenotes the fraction of allmember trades being passive,

and Passive volume is its volume‐weighted version (volume is measured in SEK). In

general,marketmakersareexpectedtotrademorepassivelythanothertraders.Thus,if

ourHFTdatasetisdominatedbymarketmakersweexpecttheHFTstobemorepassive

thanthenon‐HFTs.SimilarmetricsarereportedbyKirilenkoetal.(2011)andJovanovic

andMenkveld(2011).

3.2 HFTandnon‐HFTtradingactivity

Table 2 displays trading and order volumes aswell as themetrics introduced above,

calculatedforHFTsandnon‐HFTs.ResultsforAugust,2011,whichwasamonthofhigh

volatility, are given in Panel A; and results for February, 2012, which was a calmer

month,areinPanelB.Thebottomthreerowsofeachpanelcontainp‐valuesoftestsfor

differencesinmean,median,andstandarddeviationsbetweenHFTsandnon‐HFTs.

During August, 2011, the HFTs in our sample represent on average about 30% of all

trades in the OMXS 30 stocks. The fraction of trades is somewhat lower in February,

2012;26%.Both figuresaremuch lower than thealmost70% thatBrogaard (2011b)

reports for US markets (NASDAQ and BATS). Any comparison of such numbers is

howevercomplicatedbythefactthathybridmemberfirms(withbothHFTandagency

services)areexcluded.Hybridmembersrepresentonaverageabout40%ofthetrading

volumeinbothAugust,2011,andFebruary,2012.Giventhat53.0%ofthehybridfirm

tradingvolumeisenteredusingalgorithmicUSERIDs(seeSection2.3),wecanconclude

thattheHFTtradingactivityintheOMXS30stocksrangesbetween26%and52%.

Interestingly, the share of limit order submissions traced to HFTs is on par with the

trading activity at around 30%. This indicates that the HFTs in our sample do not

overflowthemarketwithlimitordersataratehigherthanaverage.Still,theq/tratiois

17

significantly higher forHFTs than for non‐HFTs (both in terms ofmean and terms in

medians). For both trader groups the q/t ratio is slightly higher in themore volatile

month(August2011).

The tworatios indicating liquidity supplybothshowthatHFTsaremorepassive than

non‐HFTs. The differences are statistically significant for both months, and both for

meansandmedians.ThisisasignofmarketmakingactivityamongHFTs.Asalltrades

have one passive and one active party, the average ratio across all traders should be

equal tounity.Theratiosreported indicate thatHFTssupplymore liquidity than they

demand, thatnon‐HFTssupplyroughlyasmuchas theydemand,andaccordingly that

thethirdgroupoftradersarenetdemandersofliquidity.TheHFTgroupliquiditysupply

ratios around 55‐59% are much lower than the 74‐79% observed by Jovanovic and

Menkveld(2011)foroneHFTmarketmakertradingDutchstocksatChi‐X.Ourresults

areonaverageslightlyhigher,however,thanthoseofKirilenkoetal.(2011),whoreport

trade‐weighted (volume‐weighted) liquidity supply ratios of around 50% (54%) for

theirHFTs.

The|Inventory|metricalsoshowsthatHFTstosomeextentaremarketmakers,asthe

ratio is significantly lower than that of non‐HFTs in both months. The fact that the

|Inventory| ratios on average lie in the interval of 0.25‐0.35 indicates, however, that

marketmakingisnotthesoleactivityintheHFTgroup.JovanovicandMenkveld(2011)

reportthattheirChi‐Xmarketmakerclosewithzeroinventoryin33‐60%ofthetrading

days.

Finally,ourinvestigationclearlyshowsthatHFTsareindeedfasterthantheirnon‐HFT

peers.Boththeminimumlatencyandtheaverageorderlifetimearesignificantlylower

forHFTs.Accordingtotheory(see,e.g.,Biaisetal.,2011),theabilitytoadaptfasterto

18

news(infundamentalsaswellasorderflows)putsHFTsataninformationaladvantage,

allowingthemtoextractanadverseselectioncostfromslowertraders.Suchprofitsare

dividendspaidtowardsHFTinvestmentsininformationtechnology.

Whatisimportanttonotewheninterpretingthetimedimensionisthattherearehuge

differencesbetweenmeansandmedians.Limitordersthatareleftintheorderbookfor

awholedaycanhaveasubstantial impactonthemean,buttypicallyleavethemedian

unaffected. When focusing on the medians, we note that both HFTs and non‐HFTs

improved their latency fromAugust2011 toFebruary2012, reflecting the continuous

investmentsininformationtechnology.ThegapbetweenHFTsandnon‐HFTsdecreased

but remained statistically significant in February 2012. The limit order duration

increasedforbothgroups,perhapsbecauselowervolatilitydecreasestheneedfororder

updates.

The standard deviations presented in Table 2 describe variability across days and

stocks.On several accounts, thevariability ishigh.Given that the results reportedare

aggregatemeasuresacrossalargenumberofmemberfirms,thisvariabilityisnotlikely

to be due to individualmember behavior. Furthermore, there is ample evidence that

HFTfirmstendtohavecorrelatedtradingstrategies(Brogaard,2011b;Chaboudetal.,

2009; Hendershott and Riordan, 2011b). In the next subsection, we study how

variabilityinaggregateHFTactivityisrelatedtomarketquality.

3.3 HowHFTactivitycorrelatestomarketquality

We employ panel regressions to investigate how HFT trading activity is related to

marketqualitymeasures.Intheregressionanalysis,weseektoexplainthevariabilityin

fourdependentvariables:HFTtotalSEKtradingvolume,HFTactiveSEKtradingvolume

(volume of tradeswhere theHFT initiates the trade by posting an executable order),

19

HFTpassiveSEKtradingvolume,andHFTlimitordersupply.Eachvariableisdefinedas

the fraction of the sum of HFT and non‐HFT activity, rather than as a fraction of all

activity. Themotivation for excluding the hybrid trader group is that it containsHFT

activity. Hence, benchmarking to the non‐HFT group gives a cleanermeasure of HFT

activity.

Inourgeneralpanelregressionframework,eachdependentvariableisdenoted , ,

referringtoobservationsforstocki(i=1,...,30)ondayt(thereare23and21tradingdays

in August 2011 and February 2012, respectively, yielding t=1,…,44). Each panel

regressionmodeltakesthefollowingform:

(1) , , ,

where , is a k‐vector ofmarket quality variables, and , are error terms. The

parameter represents the overall constant term in the model, whereas represents

stock‐specific fixed effects. The vector contains k regression coefficients. The error

termsareallowedtofollowageneralauto‐regressive(AR)processaccordingto:

(2) , ∑ , ,

where is the autocorrelation coefficient of order r, and the innovations , are

independentlyandidenticallydistributed.

ThecoefficientsinEquations(1)and(2)areestimatedusingthetwo‐stepcross‐section

seemingly unrelated regression (SUR) technique, allowing the error terms to be both

cross‐sectionally heteroscedastic and contemporaneously correlated. In addition, the

standard errors are computed with a White‐type technique, where the coefficient

covarianceestimator is robust to cross‐section (contemporaneous)correlationaswell

20

as arbitrary, unknown forms of different error variances in each cross‐section (see

Arellano, 1987, andWhite, 1980). TheAR lag‐length is chosen in a step‐wise fashion,

adding coefficients until the Ljung‐Box (LB) test results in a non‐rejection of the

hypothesisthattheresidualsarenotauto‐correlatedupto10lags(atthe5%level).

Thedailymarketqualityvariablesincludedin , areSIXVX,avolatilityindexforthe

OMXS 30 index; stock‐specific one‐minute realized volatility; average relative bid‐ask

spread; trading volume in million SEK; and depth, defined as the average volume

required to move the price by 1%. We also include log market capitalization (MC,

expressedinmillionSEK)asmeasuredattheendofeachmonth.InTable3,wepresent

panelregressionresultsforHFTtradingandquotingactivities.

For aggregate trading volume, HFT activity is positively correlated to volatility. This

effect is seen both for market‐wide (SIXVX) and stock‐specific volatility. HFT trading

volumeisnegativelyrelatedtoliquidity(increasinginspreadsanddecreasingindepth),

tradingvolume,andsize(MC).Ininterpretingtheseresultsitisimportanttoemphasize

that we analyze contemporaneous correlations. Thus, the results presented here is

neither evidence that HFTs increase their trading activity in volatile and illiquid

markets, nor thatmarket quality falls when HFTs increase their trading activity. The

conclusion thatwecandraw is thatamong theOMXS30stocks,HFTs traderelatively

more(highervolumes)insmallerstockswithlessliquidorderbooks,lessvolume,and

morevolatileprices(alltheseresultsarestatisticallysignificant).

Separatingthetradingvolumesintoactiveandpassivetrading,weseethatthevolatility

effect is due to active volume. That is, HFTs tend to demand more liquidity (active

volume)involatilemarkets,whereastheliquiditysupply(passivevolume)isunaffected

by volatility. The effects related to bid‐ask spreads, size, and trading volume are, in

21

contrast,duetothepassivevolume(activevolumeeffectsarenotstatisticallydifferent

fromzero).Thenegativerelationbetweentradingvolumeanddepthisconsistentacross

activeandpassivevolumes.

Inhisday‐levelOLSregressions,Brogaard(2011b) findsimilareffectswithrespect to

market‐wide volatility, but opposite effect of stock‐specific volatility. Furthermore, in

contrast toourresults,he finds thatHFTactivity is increasing in liquidityandmarket

cap. The results can, however, not be directly compared. Themarket capitalization of

firms inBrogaard’s (2011b)data set ranges from81 to197012millionUSD,whereas

firmsinourdatasetare lessdiverse,rangingfromroughly355to52600millionUSD.

Furthermore, the panel regression specifications differ substantially, as we consider

fixed effects and control for autocorrelation in the error terms. Brogaard’s (2011b)

results are in linewith the stylized fact thatHFTs stayaway fromorderbookswhere

liquidity and trading volumes are so small that their advantages in speed can not

materialize. Our results, on the other hand, show that provided that there is enough

liquidity,HFTshaveacomparablyhighshareoftheactivityinslower,morevolatile,and

illiquidorderbooks.

Wenowturn fromtradingvolumes to thevolumesof limitordersubmissions.Byand

large,theeffectsseenforlimitordersareinlinewiththepassivetradingvolumeresults:

decreasing with liquidity, trading volume, and size. Order submissions are also

positivelycorrelatedtomarket‐widevolatility,whichisnotthecaseforpassivetrading

volume.Thesimilareffectsforpassivetradingvolumesandlimitordersubmissionsare

in line with market making strategies, where quotes are continuously updated and

tradingispredominantlyonthepassiveside.Inthenextsection,weseektodistinguish

HFTsthatareprimarilymarketmakersfromotherHFTs.

22

4 DistinguishingmarketmakersfromotherHFTs

Sofar,allourresultsarereportedforHFTsandnon‐HFTsasgroups.Thisapproachisin

line with the current HFT literature, but it implicitly assumes that these groups of

tradersaremoreor lesshomogenouswithrespecttotradertypes.Asouruniquedata

setallowsustoobservethebehaviorofeachmemberfirmseparately,wenowturnto

disaggregationoftheHFTgroup.Asdiscussedintheintroduction,suchdisaggregationis

encouragedintheregulatorydebate.Inthissectionweintroduceameasureofmarket

makingactivitythatallowsustoseparateHFTmarketmakersfromopportunisticHFTs.

ThisenablesustostudythedifferencesbetweenthesedistinctHFTtradercategories.13

Furthermore, we investigate howmarket making and opportunistic trading activities

varyacrosssegmentsofstocks.

4.1 Measuringmarketmakerpresence

To investigate the degree ofmarketmaking amongHFTswe study the prevalence of

eachHFTmemberfirmatthebestbidandaskprices(theinsidequotes).Foreachstock,

wetakesnapshotsoftheorderbookeach10second‐periodineachtradingdayinour

two‐month sample. As algorithmsmay post limit orders cyclically, for example at the

turnofminutesorseconds,werandomizetheorderbooksnapshottimessothatweget

observations each 10 seconds uniformly distributed across 10 seconds. The Market

making presence for each HFTmember is then calculated daily for each stock as the

fractionof snapshotswhere thememberhasa limitorderpostedateither sideof the

insidequotes.

13Ofcourse,memberfirmscategorizedasmarketmakersorarbitrageursarenothomogenouseither.Ourdata set allows observation of each member firm in isolation, but for confidentiality reasons we arerequired to aggregate our results to member groups. If our disaggregation of HFTs is successful, theheterogeneityissignificantlyhigherintheHFTgroupthaninitssubgroups.

23

OurmeasureofmarketmakingpresenceisanalogoustoameasureappliedbyNOMX‐St

inanewpricingschemeformarketmakersintroducedinApril2012.Similarmeasures

are used by Brogaard (2011b) and Hendershott and Riordan (2011a), but with the

importantrestrictionthattheymeasuretheaggregatepresenceofHFTsandalgorithmic

traders,respectively,ratherthanthepresenceofindividualmembers.

TheresultsonthedegreeofmarketmakingarepresentedgraphicallyinFigure1,with

findingsforAugust2011inPanelAandforFebruary2012inPanelB.Thegraphcanbe

interpreted as a three‐dimensional bar chart,with OMXS 30 stocks on the x‐axis and

HFTmembers on the z‐axis. TheHFTmembers are sortedby their averagedegree of

marketmakingacrossstocksandtradingdays,andthestocksarepresentedinrandom

order (names of stocks and members are dropped for confidentiality reasons). The

marketmakingpresenceaveragedacrosstradingdaysineachmonthisdisplayedonthe

y‐axis.

ThestrikingresultofFigure1 is thatcontinuousmarketmaking inOMXS30stocks is

concentratedtoahandfulofHFTmemberswhohaveordersattheinsidequotesmore

than20%ofthetime.Thisgroupofmarketmakersishighlyactiveacrosstheboardof

stocksinoursample.OtherHFTmembersarepresentattheinsidequotessporadically,

butrarelyonthecontinuousbasisassociatedwithmarketmaking.Thegeneraltendency

ofconcentrationofmarketmakerstoafewmembersisconsistentacrossAugust2011

andFebruary2012,thoughthereareslightdifferencesinthememberidentitiesinthe

marketmakergroup.Weconcludethatmemberswithmorethan20%marketmaking

presence on average across stocks are likely to have market making as their main

business model. We classify members with lower degree of market making as

24

opportunistic HFTs who are likely to employ arbitrage or directional strategies.14

Accordingly,we now subdivide ourHFT sample into two groups,marketmakers and

opportunistictraders,andcomparetheiractivitiesintermsoftradeandquotevolumes,

inventoryratio,quotingintensity,tradingspeed,andliquiditysupply.

4.2 MarketmakerandopportunistictraderHFTactivity

Table 4 provides a comparison of trading activity among market making HFTs and

opportunisticHFTs. The trading volume and number of limit orders are presented as

fractions of all HFT activity (i.e., for now, we do not consider non‐HFT activity). The

Market maker presence, |Inventory|, q/t, Minimum latency, Limit order duration, and

Liquidity supplymetricsaredefinedasabove. Toconservespace,wehenceforthomit

meansandstandarddeviations,reportingmediansonly.TheupperthreerowsinTable

4present results forAugust2011,whereas the lower three rowsholdFebruary2012

results. Thep‐valueof theWilcoxon rank sum test reported for eachmetric andeach

month shows the probability that market makers have the same median as

opportunistictraders.

Tounderstandourmeasureofmarketmakingpresenceproperly,consideronestockat

one trading day. For each such stock‐day, we calculate the average presence of each

memberclassifiedasamarketmakerandopportunistictraderrespectively.Formarket

makers, themedian of such stock‐days is 58% in August 2011 and 70% in February

2012.Thatis,inanygiveninstanceinanyoftheOMXS30stocks,itislikelythateachof

themarketmakingmembershave at least oneorderpostedat the insidequotes.The

low market making presence recorded for opportunistic HFTs, less than 1% in both

months,indicatesthatoursubdivisionofHFTtradertypesissuccessful.

14Infutureversionsofthispaperweplantodisaggregatethisgroupfurther.

25

Brogaard (2011b) measure BBO presence on an aggregate level, i.e., the fraction of

calendar time when any of the HFTs in his sample has quotes posted at the inside

spread.Ourmeasure, incontrast, isthemarketmakerpresenceaveragedacrossHFTs.

ForhissampleofUSstocks in2010,Brogaard(2011b)reportsmediansof56%,60%,

and94%forsmall‐,medium‐,andlarge‐capstocksrespectively.Ourevidenceshowthat

disaggregation of such numbers into individual members uncovers strong diversity

amongHFTs.

LookingatHFTtradingactivity,Table4showsthatthelionshareoftheHFTactivitycan

be traced tomarketmaking. InAugust2011,71.5%of theHFT tradingvolume (SEK)

and80.5%ofthelimitordertrafficwasduetomarketmakeractivity.InFebruary2012,

theshareofHFTtradingvolumeformarketmakerswaslower(62.8%),buttheshareof

limit orderswashigher (86.4%). To our knowledge, this is the first evidence of how

HFTactivityisdistributedbetweenmarketmakersandopportunistictraders.Asmarket

makingactivityisregardedaspositiveformarketquality,whereasopportunistictrading

canpotentiallyamplifyprice fluctuations, thedistributionofHFTactivity is important

forpolicymaking. If thedistributionshowedhere is representative forothermarkets,

any policy making HFT activity in general more expensive, such as the proposed EU

transactiontax,wouldprimarilyhitmarketmakingactivity.

In accordance to the trading activity figures, q/t ratios are significantly higher for

marketmakersthanforopportunistictraders.ThedifferenceisincreasingfromAugust

2011toFebruary2012.Thisconfirmsthatmarketmakersaremuchmorepassivethan

arbitrageursandmomentumtraders.GiventhatHendershottetal.(2011)andBoehmer

etal. (2011)base theirATproxiesonquotingactivity,ourevidencemay indicate that

26

the market quality effects that they associate with AT is more likely to be effects of

marketmakingthanwithotherATstrategies.

The Liquidity supply ratios show thatmarketmakers are on the passive side in 68%

(70%) of their trades in August 2011 (February 2012), compared to the 74‐79%

reportedbyJovanovicandMenkveld(2011).Theloweraveragepresenceforourmarket

makersmaybeduetotheincreasingcompetitioninHFTmarketmaking(Jovanovicand

Menkveld,2011,cover77tradingdaysin2007and2008).Theaverage|Inventory|level

at 5% and 7% in the two months is in line with Jovanovic and Menkveld’s (2011)

characterizationofHFTmiddlemenwithstronginventorymean‐reversion.Accordingto

their theoreticalmodel,which theybackupwith empirical evidence, suchmiddlemen

have positive impact on social welfare (under all reasonable parameter values). The

muchhigher|Inventory|recordedforopportunistictradersmaybeseenasevidenceof

inter‐marketarbitrageactivities.

TheminimumlatencyofmarketmakersisthesameinAugust2011andFebruary2012,

at0.1milliseconds.Opportunistictradersimprovetheirminimumlatencyovertime,but

are significantly slower than market makers. The low latency observed for market

makersservesasanillustrationofthecompetitioninliquiditysupplyinmodernequity

markets. Unless market makers respond immediately to news, they risk that their

outstanding quotes are picked off by faster traders. The pick‐off risk translates to

adverse selection costs, forcing the market maker to charge wider (uncompetitive)

spreads.ForHFTswithstrategiesrelyingonactiveratherthanpassivetradinglatency

maybelesscritical,whichisreflectedinourresults.

The average limit order duration is longer in the less volatile month and longer for

marketmakers than foropportunistic traders.Thiseffect is likely related to themore

27

passive strategy of market makers, and that quotes require less updating in a less

volatilemarketenvironment.

Overall,thedifferencesreportedbetweenmarketmakersandopportunistictradersare

substantial.ForseveralmetricsthedifferencebetweentheHFTgroupsarelargerthan

thedifferencesbetweenHFTsandnon‐HFTsreported inTable2(e.g., liquiditysupply

ratiosinbothmonths).Thisshowsthatthedistinctionbetweentradergroupswithinthe

groupofHFTsisjustasimportantasthedistinctionbetweenHFTandnon‐HFTactivity.

4.3 HFTactivityindifferentsegmentsofstocks

TofurtherimproveourunderstandingofHFTactivitywenowseparatemarketmaker

andopportunistictradingactivityinvarioussubsamplesofstocks.Thepurposeofthis

analysis is toseewhether thedifferent typesofHFTschoose thestocks inwhich they

areactiveaccordingtotheaggregatestockactivityandstockmarketquality.

ForeachsamplemonthweranktheOMXS30stocksaccordingtotheirexpostmarket

capitalization, tradingvolume,realizedvolatility,andrelativebid‐askspread.Thedata

for theserankingsare taken fromNASDAQOMXNordic’shomepage;seetheappendix

for a stock‐by‐stock summary for each month. For each ranking variable, we let the

upper and lower third of the sample constitute subsamples, for which we report

mediansoneachofthemetricsappliedinTable4.TheresultsaregiveninTable5,with

marketmaker activity in Panel A and opportunistic trader activity in Panel B. The p‐

valueofthez‐testperformedforeachmetricandeachsortingindicatestheprobability

thattheupperandlowerthirdsofstockshavethesamesamplemedian.Forbrevity,the

presentedresultsareestimatesacross the twomonths.Overall, the results forAugust

2011 and February 2012 are qualitatively the same (the month‐specific tables are

availablefromtheauthorsuponrequest).

28

OuranalysisinPanelAshowsthatthemarketmakerpresenceissignificantlyhigherin

stocks with higher market capitalization, higher trading volume, lower realized

volatility, and higher relative bid‐ask spread. These results are in line with market

makingtheory(seeMadhavan,2000,foranoverview).Marketmakersprofitfromhigh

volumes and large spreads. Market making is more expensive in volatile stocks as

inventory costs are increasing with risk; and in smaller stocks because they are less

transparent,makingprivateinformationmorelikely.

Interestingly, increased market maker presence does not imply increased liquidity

supply relative liquidity demand. Quite the opposite, market makers demand more

liquidity(relativetheirsupply)inthesegmentswheretheyhavehighermarketmaking

presence(seeLiquiditysupplyinTable5,PanelA).Thiseffectisseenforbothnumberof

orders and for order volumes, and is statistically significant in all but one case.

Accordingly, themarketmakerq/t ratio is lowerwhen themarketmakerpresence is

higher. In general, market makers demand liquidity either to offload unwanted

inventorypositionsortoaccumulateinventoryattheexpectationofapricechange(as

arguedbyKirilenko et al., 2011). Largeunwanted inventorypositions canbebuilt up

when there is a high correlation in the direction of trades, e.g., when institutional

investorsexecutelargeorders.Thus,thehighmarketmakerusageofmarketordersin

large‐cap, high‐volume, and low volatility stocks may be due to that such stocks are

moretargetedbyinstitutionalinvestors.Alternatively,marketmakersareleadingprices

moreinthesestocks,takingliquidityaheadofexpectedpricechanges.

Foropportunistictraders,thetradingandlimitordervolumesarebydefinitionoffsetby

themarketmakingvolumes.Fortheliquiditysupplyratiosthereisnomechanicrelation

to market maker results, but the tendency is nevertheless that effects observed for

29

opportunistictraders(seePanelB)areoppositetothoseobservedformarketmakers.

Opportunistictradersrecordhigher liquiditydemandratiosinstockswith lowmarket

capitalization, low trading volume, high volatility, and large spreads. Their minimum

latencyinthesamestocksishigherthanotherwise,buttheaveragelimitorderduration

is lower.Forarbitrageurs, themain incentive forpostinga limitorder is theability to

offsetapassivetrade(whenthelimitorderishitbyamarketorder)atonemarketbyan

active trade at a better price at another market (e.g., another venue or a related

product). ForOMXS30 stocks, the activepart of the strategy is likely to takeplace at

NOMX‐St,asittendstobemoreliquidthanMTFandderivativesmarketswhererelated

assetsaretraded.Hence,thehigheropportunistictraderliquiditydemandseeninsmall,

low turnover, high volatility, and high spread stocks may be explained by higher

arbitrageactivity,resultinginmoreliquiditydemandatNOMX‐St.

For stockswith high relative bid‐ask spreads, bothmarketmakers and opportunistic

tradershavesignificantly lower liquiditysupplyratios.This iscounter‐intuitive,asthe

costofcrossingthespreadinsuchstocksishigher.

TheanalysispresentedinthissubsectionshedslightonthebehaviorofHFTsindifferent

segments of stocks. For policy‐making, a more important question is arguably what

causalinfluenceHFTactivitieshaveonmarketquality.Weaddressthisquestioninthe

nextsection.

5 IMPACTONMARKETQUALITY:ANEVENTSTUDY

An inherent problem when assessing the influence of HFT on market quality is the

difficultyofdeterminingthedirectionofcausality.Itiswell‐knownthatHFTstrategies

tend to focus on the most liquid stocks in each market, where efficiency is typically

30

higher and volatility smaller than in less liquid stocks. To isolate the effect of HFT

strategies,wehenceneedto findevents thatareexpected tochangeHFTdirectly,but

thatarenotexpectedtochangemarketquality(suchmethodologiesareappliedforAT

by, e.g.,Boehmeret al., 2011; andHendershott et al., 2011; and forHFTbyBrogaard,

2012).IfwecanidentifysucheventswecanclaimthattheyconstituteexogenousHFT

shocks,andthenwecanstudytheconsequencesformarketquality.

We use tick size changes as instrument for HFT shocks. According to Grossman and

Miller(1988)andHarris(1997),aticksizereductionbenefitsliquiditydemandersdue

tosmallertradingcosts(narrowerbid‐askspreads),while itcauses liquiditysuppliers

to be lesswilling to provide liquidity.Moreover, Goldstein and Kavajecz (2000) state

thatticksizechangeshaveimportantimplicationsforthesupplyofliquidity,tradingby

differentmarketparticipants,andordersubmissionstrategies.WearguethatHFTsare

profoundlyaffectedbyticksizechanges,andmoresothannon‐HFTs(retailinvestors).

AtNOMX‐St,theminimumpriceincrementdiffersbetweenstockswithdifferentprices.

Forexample,astocktradingatapriceaboveSEK50butbelowSEK100hasaminimum

ticksizeofSEK0.05.AstocktradingbelowSEK50,ontheotherhand,hasaminimum

ticksizeofSEK0.01.15ThisisimportantforHFTs,becausewhenastockpricegoesfrom

SEK49toSEK51,theminimumbid‐askspreadincreasesfromaround0.2%toaround

1%. At the SEK 51 price level, market making is much more profitable while other

strategies,suchasarbitrage trading,aremorecostlywith thewiderminimumbid‐ask

spread.Thus,wepredict that an increase in the tick sizewill increasemarketmaking

HFTactivitiesanddecreaseopportunisticHFTactivities.Thiswouldbeinlinewithour

15NOMX‐StappliesthresholdpricelevelstodetermineminimumticksizesaccordingtotheFederationofEuropeanExchanges (FESE),Table2, for theOMXS30 stocks.Forourevent study, apply the followingminimum tick sizes: 0.01, 0.05, and 0.10, for the following price ranges: 10.0000 – 49.9900, 50.0000 –99.9500,and100.0000–499.9000.

31

empirical result presented above.Adecrease in the tick sizewouldhave theopposite

effect. Retail investors (non‐HFTs) are likely to be affected by tick size changes in a

similarmannerasopportunisticHFTs,but toa lesserdegree.The influenceof trading

costsdecreaseswiththeinvestmenthorizon(seee.g.,ChalmersandKadlec,1998),and

retail investors are expected to have much longer investment horizons than

opportunisticHFTs.

5.1 Theevents

Weconstruct the events in the followingmanner. First,we identify an eventday as a

trading day when a stock’s transaction prices correspond to different tick sizes, i.e.,

when the stock price is either increasing or decreasing enough to cross a tick size

boundary.Second,we identify the lastdaybeforeandthe firstdayafter theeventday

whentransactionpricesstayinthesameticksizecategorythewholeday.Ifnosuchday

isfoundwithintentradingdaysbefore/afterthepotentialevent,theeventisdiscarded.

Moreover,theeventisdiscardedifthedaybeforeandthedayafterhavestockpricesin

thesameticksizecategory.Thatis,theeventisonlyvalidifitleadstoapricethatstays

inthenewticksizecategoryforatleastonefulltradingday,withintentradingdays.

WelimittheeventstudytothestockscomprisingtheOMXS30indexduringtheperiod

betweenFebruary8,2010,andMarch31,2012.16Thefirstdatemarkstheintroduction

oftheINETlow‐latencytradingplatform,andthelastdateisthelasttradingdaybefore

a new liquidity programwas introduced, where the trading costs formarketmaking

HFTswerelowered.Duringthesampleperiod,weidentify89validevents;whereof42

constitutedowntickevents,withadecreasingstockprice,and47uptickeventswhere

thestockpriceisincreasing.Inaddition,wedividethesampleintotwoparts;beforeand

16TherearenochangesinthecompositionoftheOMXS30indexduringthetimeperiodconsidered.

32

after January 1, 2011. Before that data, none ofmarketmakers identified in ourHFT

sample had yet entered NOMX‐St. Thus, the sample division enables us to analyze

market quality effects from events after January 1, 2011, where both market maker

HFTs and opportunistic HFTs are active and from events before that date where no

marketmakingHFTsaretradingtheeventstocks.Thefirst second partofthesample

contains42 47 observations;whereof25 17 aredowntick events and17 30 are

uptickevents.

5.2 AbnormalHFTactivityintheevents

ToascertainthattheticksizeeventisavalidinstrumentforshockstoHFTactivities,we

measure changes inHFTactivity in each event (treatment) stockbetween thebefore‐

date and the after‐date. In order to control for potential time‐series variation in HFT

activities intheeventstocks,wealsoobtaintheaverageofthecorrespondingchanges

for all other OMXS 30 (control) stocks.17 In addition, we contrast changes in HFT

activities to changes in corresponding non‐HFT activities using a difference‐in‐

difference‐in‐differencetypeofanalysis.Inthisrespect,wedefineanabnormaleffectas

HFTactivityrelativethesumofHFTandnon‐HFTactivity,inastockthatexperiencesa

ticksizechangeevent(Treatment),afterrelativetobeforetheevent,relativetheaverage

correspondingfractionofactivityinnon‐eventstocks(Benchmark).Duringthesecond

partofthesample,wealsoseparatebetweentwotypesofHFT;namelymarketmaking

(MM) and non‐market making (NM) or opportunistic HFT. For example, abnormal

tradingvolumeforeventiandHFTtypej(j=MM,NM)is:

17 Stocks that are involved in another qualified event with before‐ and after‐dates that to any extentoverlapwiththecurrentevent’sbeforeandafterdatesareexcludedfromthecontrolgroup.

33

(3)% ,

% ,

% ,

% ,

where % , , / , , ; % , , / ,

, ; , and , are the HFT volumes before and after the event; and

, and , arethenon‐HFTtradingvolumesbeforeandaftertheevent.

Apart from trading volumes, we also consider the following HFT activities in the

analysis:thenumberofexecutions,thenumberofadditionstotheorderbook,andthe

numberofcancellationsfromtheorderbook.Inaddition,wedistinguishbetweenactive

andpassivetradingvolume(numberofexecutions),whereHFTsintheformercaseare

actively submitting marketable orders, and in the latter case are passively hit by

incomingorders.ForeachtypeofHFTactivity,weobtainanabnormalmeasureofMM

andNMactivitybyapplyinganexpressionsimilartotheoneinEquation(3).

TheresultsareprovidedinTable6.Startingwiththedowntickevents,theNM‐HFTsare

in relative terms reactingwith increased trading activities. During both the first part

(2010) and the second part (2011) of the sample, NM‐HFT median abnormal total

volumeandmedianabnormaltotalexecutionsiseachpositiveandsignificantlydifferent

fromzero.Evidently,adowntickeventtriggersNM‐HFTstoincreasetradingactivitiesin

theeventstocks,morethannon‐HFTs(andMM‐HFTsinthesecondpartofthesample),

and more than in other stocks that are not subject to a concurrent tick size change.

Moreover,whenseparatingbetweenactiveandpassivetradingvolumeandnumberof

executions, we note that each median abnormal active trading activity is highly

significantly different from zero,whereas each correspondingmedian passive trading

activityisnotsignificantatthe5%level.Themedianabnormalnumberofadditionsand

medianabnormalnumberofcancellationsissignificantlydifferentfromzeroatthe5%

34

level in2010,butnotin2011, indicatingthatadowntickeventdoessignificantlyalter

NM‐HFTs’ order submission and cancellation strategies only in the first part of the

sample,i.e.,whennoMM‐HFTsareactive.

For thedowntick events, and in the secondpart of the sample, eachmedianMM‐HFT

activityis,whensignificantlydifferentfromzero,negative.Thisimpliesthatadowntick

event triggersmarketmakingHFTs to decrease trading activities in the event stocks,

morethanNM‐HFTsandnon‐HFTs,andmorethaninotherstocksthatarenotsubjectto

a concurrent tick size change. The median abnormal MM‐HFT total trading volume

(numberofexecutions)inthedowntickeventsissignificantlynegativeatthe5%level.

Interestingly,whenseparatingbetweenactiveandpassivetradingvolumeandnumber

of executions, we note that each median abnormal passive trading activity is

significantly different from zero at the 5% level,whereas each correspondingmedian

active trading activity clearly is not. However, each median abnormal number of

additionsandabnormalnumberofcancellationsisnotsignificantlydifferentfromzero.

TheresultsfortheuptickeventsindicatethatNM‐HFTsreducetheiractivities.Eachof

themedianabnormaltradingvolumeandthemedianabnormalnumberofexecutionsis

significantlylowerthanzeroatthe1%levelinboththefirstpartandthesecondpartof

thesample.Forbothpartsofthesample,themedianofactiveabnormaltradingvolume

andthemedianofactivenumberofexecutionsarebothsignificantlynegative,whereas

the corresponding medians of passive activities are not. Moreover, the results show

evidence in favorofNM‐HFTssignificantly (at the1% level) increasing thenumberof

orderadditionsandcancellationsintheuptickevents,butonlyduringthefirstpartof

thesample.Finally,wefindnoevidenceshowingthatMM‐HFTschangetheiractivitiesin

theuptickevents.

35

The results in Table 6 are consistent with our prediction that the events of tick size

changes affectHFT activities to a larger extent thannon‐HFT activities. Thus,we find

supportforthenotionthatwecanusetheticksizechangesasanexogenouseventfor

HFT activity. Moreover, our findings show that opportunistic HFT adjust their active

trading inresponse to ticksizechanges, in linewith theprediction thatactive trading

strategiesaremore(less)profitablewhentheticksizeissmaller.MarketmakingHFTs,

incontrast,respondtodowntickeventsbyadjustingtheirpassivetrading,presumably

asmarketmakingislessprofitableatsmallerticksizes.Theirtradingvolumeresponse

touptickevents isasexpectedpositive,butnotsignificantlydifferentfromzeroatthe

5%confidencelevel.

InthereviewofMiFID,theEuropeanCommission(2010)hasconsideredminimumtick

sizesasaregulationtool.Ourresultsshowthatinsofarsuchregulationswouldincrease

tick sizes, market making HFTs would increase their activity whereas opportunistic

HFTswoulddecreasetheiractivity.

5.3 Marketqualityanalysis

Having established that the tick size event constitutes a valid instrument for HFT

activity, we proceed to analyze the effects on market quality within the event study

framework. We consider two dimensions of market quality: intraday volatility and

market making activity. While market making activity is directly associated with the

liquidity supply of market making HFTs in the events, we analyze more closely if

changesinHFTcausetheintradaystockreturnvolatilitytochange.Forthatpurpose,we

calculateintradayrealizedstockreturnvolatility,usingmidpointquotechangesin1,5,

10,and15minuteintervals.Let denotetheaverageofintradayrealizedvolatility,

fork=1,5,10,and15.Foreachlevelofk,wecalculate foreachstockonadaily

36

basis. Using a difference‐in‐difference approach, we define abnormal volatility as the

volatilityofastockthatexperiencesaticksizechangeevent(Treatment),afterrelative

to before the event, relative the average corresponding volatility of non‐event stocks

(Benchmark)accordingto:

(4) ,

,

,

,

where , , isthevolatilityafter(before)theeventi.

Table7presentstheresultsofthevolatilityanalysiswithintheeventstudyframework.

Weseparatebetweendowntickanduptickeventsaswellasthetwoperiodsbeforeand

afterJanuary1,2011.Forthedowntickeventsduringthefirstpartofthesample(2010)

we note a negative, but not significantly different from zero, abnormal volatility.

Consequently,theincreaseintradingactivitiesoftheopportunisticHFTsdoesnotcause

anincreaseinstockreturnvolatility.Duringthesecondpartofthesample(2011),when

opportunisticHFTs increasetheirtradingactivitiesandmarketmakingHFTsdecrease

theiractivities,weobserveaslightlydifferentpicture.Whiletheabnormalvolatility in

mostcasesisnotsignificantlydifferentfromzero,theabnormalvolatilityona10minute

basis is weakly significantly positive (at the 10% level). This could be seen as an

indication that the combined increase in opportunisticHFT activities and decrease in

marketmakingHFTactivitiesleadtoanunchanged(oratleastonlyaslightincreasein)

volatility.

The uptick events during the first part of the sample (2010) are associated with a

significantly positive abnormal volatility (at the 5% level).Hence,whenopportunistic

HFTs,whoaretheonlytypeofHFTsactiveduringthefirstpartofthesample,decrease

theiractivities,volatilityincreases.Interestingly,thiseffectisnotseenduringthesecond

37

sample period (2011). Instead, abnormal volatility is not significantly different from

zero following the uptick eventswhen the decrease in opportunistic HFT activities is

offsetbyacorrespondingincreaseinmarketmakingHFTactivities.

Overall,ourfindingsshowthat,intheabsenceofmarketmakersin2010,opportunistic

HFTactivitymitigatesvolatility.Intheeventsof2011‐2012,thevolatilityeffectsseenin

2010 are reversed. As the 2011‐2012 events are associatedwithmarketmaking and

opportunisticHFTsadjustingtheirtradingvolumesindifferentdirection, thevolatility

effectisacombinationofresponsestothosechangesintradingactivity.Theconclusion

isthatthedowntickeffectismitigatedvolatilityduetoincreasingopportunistictrading

andstrengthenedvolatilityduetodecreasedmarketmaking.Theoppositepatternholds

for uptick events. Thus,we conclude that bothmarketmaking andopportunisticHFT

activitymitigatevolatility.

6 Policyimplicationsandconcludingremarks

Thispaperbridgesthegapbetweentheacademicliterature,whichtodatetreatsHFTs

as a homogenous group of traders, and the regulatory debate,which emphasizes that

different HFT strategies have different impacts on market quality. By distinguishing

marketmaking fromotherHFT strategies,we take the first step to empirically assess

how the market impacts of HFT strategies differ. Our findings have several policy

implications:

a) We find thatamajorityof theHFT tradingvolumeandmore than80%ofHFT

limitordersubmissionsareassociatedwithmarketmakingstrategies.Thus,any

policyaimedatlimitingthescopeofHFTactivityasawholewouldprimarilyhit

marketmakingstrategies.Marketmakingstrategiesarebydefinitionpositivefor

38

liquidityprovision,andaccordingtooureventstudy,theyalsomitigatevolatility.

Thus, financial transaction taxes such as the one proposed by the European

Commission,canbeexpectedtounderminemarketquality.

b) We find that the high order‐to‐trade ratios associatedwith HFT is primarily a

marketmakingphenomenon.InAugust,2011(February,2012),marketmakers

had40% (160%)higherorder‐to‐trade ratios thanopportunisticHFTs.Market

makers continuously monitor order flows to quickly respond to news. Their

abilitytorespondquicklytonews(theirlatencyissignificantlylowerthanother

HFTs aswell as non‐HFTs) reduces their exposure to adverse selection,which

traditionallyisacostchargedbymarketmakerstouninformedtraders.Policies

directed at reducing order‐to‐trade ratios and imposing minimum limit order

durations,whichhasbeenconsideredbyboththeEuropeanCommission(2010)

andGermanauthorities,arethuslikelytolimitmarketmakersabilitytoadaptto

news.Thiswould increasetheadverseselectioncosttomarketmakers, leading

towiderbid‐askspreads.

c) TheEuropeanCommission (2010)has also considered imposingminimum tick

sizes. Our investigation of tick size changes shows that market making HFTs

increasetheiractivitywhenticksizesareincreased,whereasopportunisticHFTs

decrease their activity. Thus, as market makers are associated with more

intensive quoting than opportunistic HFTs, we would expect order‐to‐trade

ratios to increase if minimum tick sizes would be imposed. Furthermore,

accordingtoourresults,intradayvolatilityisunaffectedbyticksizechanges.

d) Overall, we find that both market makers and opportunistic HFTs mitigate

intradayvolatility.Thelattergroup,however,spansmanyHFTstrategiesandwe

39

areunabletotellwhetheranyofthosestrategiesaremalicious.Inasurveybythe

Swedish Financial Supervisory Authority many market participants expressed

worriesaboutmanipulativeHFTstrategies,suchasfront‐running,spoofing,and

layering(Finansinspektionen,2012).Futureresearchshouldbeaimedatfurther

disaggregationofHFTs,toinvestigatetheprevalenceandmarketimpactofsuch

illegalstrategies.However,ourresultsindicatethat,asagroup,theopportunistic

HFTscontributetomarketquality.

IntheUS, followingtheflashcrashofMay6,2010,theSEChas imposedstrengthened

reporting requirements on large traders, including many HFTs. The SEC has also

imposed circuit breaker rules to limit the impact of erroneous algorithms and large

orders.18 The European Commission (2010) is considering similar regulations in the

review of MiFID. As this paper does not address the problems associated with

extraordinary intraday volatility, we are unable to comment on these regulations

specifically,butingeneralwefindnoreasontobelievethattheyharmmarketmakingin

normaltimes.

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43

Figure1:MarketmakerpresenceacrossOMXS30stocksandexchangemembers

PanelA:August2011

00.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Exchange members(anonymized)

Marketmaker 

presence

Stocks(anonymized)

44

PanelB:February2012

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Exchange members(anonymized)

Marketmaker 

presence

Stocks(anonymized)

45

Figure1presentstheMarketmakerpresenceforeachexchangememberineachOMXS30stocks.Marketmakerpresenceisestimatedasthefractionof10second‐periodsinthecontinuoustradingdaythatamemberhasquotesateitherofthebestbidandofferprices,reportedasthemeanacrossacrossalltradingdaysinAugust2011(PanelA)andFebruary2012(PanelB).Onthez‐axisexchangememberidentities(MPIDs)aresortedbytheiraverageMarketmakerpresenceacrossstockseachmonth.

46

Table1:Summarystatistics

Marketcap.(MSEK)

Tradingvolume(MSEK)

Numberoftrades

Numberoftradedshares(x1000)

Numberofadditions

Numberofcancellations

DepthNominalbid‐askspread(SEK/100)

Relativebid‐askspread

(%)Volatility

Aug2011 Mean 79,340 527 7,522 4,936 261,478 302,826 9,016 12.39 0.104 0.215

Median 54,426 396 5,856 2,857 243,340 260,491 7,951 13.41 0.099 0.223

StDev 69,434 398 4,409 4,657 141,834 177,248 4,810 7.93 0.025 0.078

Feb2012 Mean 91,372 361 4,962 2,985 82,036 97,398 14,247 11.97 0.086 0.044

Median 63,476 298 4,347 1,967 68,823 63,668 13,469 12.96 0.091 0.042

StDev 77,330 210 2,122 2,662 58,412 97,599 6,589 5.82 0.021 0.021

t‐test 0.5285 0.0493 0.0058 0.0511 0.0000 0.0000 0.0009 0.8148 0.0049 0.0000

z‐test 0.3871 0.0993 0.0073 0.0215 0.0000 0.0000 0.0014 0.9941 0.0127 0.0000

F‐test 0.5657 0.0010 0.0002 0.0036 0.0000 0.0019 0.0955 0.1017 0.2615 0.0000

Table1presentssummarystatisticsforthesampleofOMXS30indexstocksduringAugust,2011,andFebruary,2012.Eachmean,median,andstandarddeviationisanequallyweightedestimateacrossstocks.Foreachmonthandeachstock;Marketcapisthenumberofoutstandingsharestimesthelasttransactionpriceofthemonth(inSEK1,000,000);Tradingvolumeisthetotalvalueofsharestradedperday(inSEK1,000,000);Numberoftradesisthenumberoftransactionsperdayduringthecontinuoustradingsession;Numberoftradedsharesisthenumberoftradedsharesperday(×1,000)duringthecontinuoustradingsession;Numberofadditions isthenumberofaddedorderspostedtothelimitorderbookperdayduringthecontinuoustradingsession;Numberofcancellations isthenumberofcancelledordersfromthelimitorderbookperdayduringthecontinuoustradingsession;Depthisdefinedasthenumberofsharesneededtobuyorsellinordertomovetheaskorbidpriceby1%,reportedastheaverageofaskandbiddepthusingend‐of‐minutesnapshotsoftheorderbookforeachstock;Nominalbid‐askspreadistheaveragebestquotedbid‐askspreadusingend‐of‐minutesnapshotsoftheorderbookforeachstock(inSEK0.01);Relativebid‐askspreadistheaveragebestquotedbid‐askspread,dividedbythespreadmidpoint,usingend‐of‐minutesnapshotsoftheorderbookforeachstock(%);Volatilityistheintradayrealizedstock returnvolatility, usingmidpointquote changes inone‐minute intervals.The last three rowscontainp‐values froma t‐test for equalitybetweenmeans, aWilcoxonranksumz‐testforequalitybetweenmediansandanF‐testforequalitybetweenvariancesacrossthetwomonths.

47

Table2:HFTvs.non‐HFTcharacteristics

PanelA:August2011

Fractionofall

tradingvolume

Fractionofalladdorders

|Inventory| q/tMinimumlatency(ms)

Limitorderduration(ms)

Liquiditysupply(volume)

Liquiditysupply(trades)

HFT Mean 0.297 0.301 0.26 15.27 1.08 3590 0.58 0.57Median 0.278 0.290 0.27 13.19 0.37 2697 0.59 0.58StDev 0.103 0.107 0.10 8.40 4.41 4113 0.09 0.09

Non‐HFT Mean 0.309 0.054 0.59 3.11 65.29 27232 0.55 0.49Median 0.301 0.047 0.59 2.01 0.80 19924 0.55 0.49StDev 0.082 0.036 0.08 2.72 340.57 34732 0.07 0.07

t‐test 0.011 0.000 0.000 0.000 0.000 0.000 0.000 0.000z‐test 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000F‐test 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

48

PanelB:February2012

Fractionofalltradingvolume

Fractionofalladdorders

|Inventory| q/tMinimumlatency(ms)

Limitorderduration(ms)

Liquiditysupply(volume)

Liquiditysupply(trades)

HFT Mean 0.255 0.315 0.35 9.55 11.85 10589 0.55 0.57Median 0.246 0.316 0.34 8.60 0.22 7998 0.55 0.57StDev 0.089 0.124 0.10 4.26 187.33 10139 0.11 0.10

Non‐HFT Mean 0.344 0.075 0.63 1.67 97.66 49846 0.51 0.50Median 0.343 0.069 0.63 1.23 0.36 34073 0.51 0.50StDev 0.080 0.043 0.08 1.27 691.42 46273 0.07 0.06

t‐test 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.000z‐test 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000F‐test 0.008 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Table2presentssummarystatisticsofthetradingandquotingactivityofHFTsandnon‐HFTsmembersinOMXS30indexstocksduringAugust2011(PanelA)andFebruary(PanelB).Eachmean,median,andstandarddeviationisanequallyweightedestimateacrossdaysandstocks.Thestock‐dayobservationsarecalculatedasfollows:TheFractionofalltradingvolume isthesumofSEKvolumetradedforallmembersineachgroup(HFTandnon‐HFT),dividedbythesumofallSEKtradingvolume;theFractionofalladdordersisthesumofaddorderspostedbymembersineachgroup,dividedbythetotalnumberofaddorders;|Inventory|isthe absolute accumulated inventory divided by the trading volume (in number of shares), reported as themedian across members in each group; q/t is theaggregatenumberofaddordersdividedbytheaggregatenumberofexecutions;Minimumlatency(Limitorderduration)istheminimum(median)lifetimeoflimitordersthatarecanceledbeforetheendofthetradingday,reportedinmillisecondsandasthemedianacrossmembersineachgroup;Liquiditysupplyisthefractionofthegroup’stradingvolumewherethememberisonthepassiveside,countedineitherSEKtradingvolumeornumberoftrades.Thelastthreerowscontainp‐valuesfromat‐testforequalitybetweenmeans,aWilcoxonranksumz‐testforequalitybetweenmediansandanF‐testforequalitybetweenvariancesacrossthetwogroups.

49

Table3:Highfrequencytraders’fractionoftradingactivity

Total

volume

Active

volume

Passive

volume

Order

additions

Intercept 1.4902 0.0844 2.5722 2.8042 (0.0001) (0.8563) (0.0000) (0.0000)

SIXVX 0.0011 0.0038 ‐0.0002 0.0007 (0.0529) (0.0008) (0.7532) (0.0340)

Marketcap ‐0.0376 0.0094 ‐0.0728 ‐0.0741 (0.0127) (0.6437) (0.0004) (0.0003)

Volatility 0.0167 0.0504 ‐0.0196 0.0042 (0.0005) (0.0086) (0.1128) (0.6074)

Spread 0.2847 0.0248 0.3238 0.2052 (0.0000) (0.7774) (0.0011) (0.0000)

Volume ‐0.0196 0.0017 ‐0.0394 ‐0.0214 (0.0019) (0.9303) (0.0000) (0.0007)

Depth ‐0.0001 ‐0.0001 ‐0.0001 ‐0.0001 (0.0000) (0.0000) (0.0000) (0.0000)

AR(1) 0.2759 0.2982 0.2552 0.3706 (0.0000) (0.0000) (0.0000) (0.0000)

AR(2) 0.0844 0.0814 0.1271 0.2332 (0.0000) (0.0000) (0.0000) (0.0000)

AR(3) 0.0981 0.1436 0.0461 0.0733 (0.0000) (0.0000) (0.0293) (0.0000)

AR(4) ‐0.0569 0.0206 (0.0008) (0.1507)

AR(5) ‐0.0362 0.0410 (0.0475) (0.0068)

LB(10) 0.2800 0.3050 0.2120 0.4200

Table 3 shows the results from fixed effects panel regressions onHFT trading activity. The dependentvariablesareexpressedasHFTactivityasafractionofthesumofHFTandnon‐HFTactivity:Activevolumeis the trade volume where the trader in question initiates the trade by posting a market order ormarketablelimitorder;Passivevolumeisthetradingvolumewherethetraderhasastandinglimitorderthat isexecutedbyamarketorder;Tradingvolume is thesumofactiveandpassivevolume;andOrderadditionsisthenumberoflimitordersposted.Thefollowingexplanatoryvariablesareused:SIXVXistheOMXS30volatilityindex;Marketcapisthelogmarketcapitalization(MSEK);Volatilityistheone‐minuterealizedvolatility;Spreadistherelativebid‐askspread;Volumeisthetradingvolume(MSEK);andDepthistheaveragevolumerequiredtomovethepriceby1%ineitherdirection.Thecoefficientsareestimatedusing the two‐step cross‐section SUR technique, allowing the error terms to be both cross‐sectionallyheteroscedasticandcontemporaneouslycorrelated.Thep‐values (inparentheses)arecomputedwithaWhite‐type technique, where the coefficient covariance estimator is robust to cross‐section(contemporaneous)correlationaswellasarbitrary,unknownformsofdifferenterrorvariancesineachcross‐section(seeArellano,1987).TheARlag‐lengthischoseninastep‐wisefashion,addingcoefficientsuntiltheLBtestresultsinanon‐rejectionofthehypothesisthattheresidualsarenotauto‐correlatedupto10lags(atthe5%level).

50

Table4:Marketmakingvs.opportunisticHFTs

Marketmakerpresence

Fractionofalltradingvolume

Fractionofalladdorders

|Inventory| q/tMinimumlatency(ms)

Limitorderduration(ms)

Liquiditysupply(volume)

Liquiditysupply(trades)

August2011(medians)

MarketmakingHFTs 0.582 0.715 0.81 0.05 14.60 0.10 3021 0.71 0.68OpportunisticHFTs 0.005 0.285 0.19 0.36 10.14 0.77 2201 0.35 0.29z‐test(p‐value) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 February2012(medians)

MarketmakingHFTs 0.697 0.628 0.86 0.07 10.36 0.10 10780 0.71 0.70OpportunisticHFTs 0.000 0.372 0.14 0.50 3.95 0.46 4366 0.31 0.28z‐test(p‐value) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Table4presentsmediansofthetradingandquotingactivityofmarketmakersandnon‐marketmakerswithinthegroupofHFTmembers,inOMXS30indexstocks,duringAugust2011andFebruary.Eachmedianisestimatedacrossdaysandstocks.Thestock‐dayobservationsarecalculatedasfollows:Marketmakerpresenceisthefractionof10second‐periodsinthecontinuoustradingdaythatamemberhasquotesateitherofthebestbidandofferprices,reportedasthemeanacrossmembersineachgroup;theFractionofHFTtradingvolumeisthesumofSEKvolumetradedforallmembersineachgroup(MarketmakersandHFTthatarenotmarketmakers),dividedbythesumofallHFTSEKtradingvolume;theFractionofHFTaddorders is thesumofaddorderspostedbymembers ineachgroup,dividedbythetotalnumberofHFTaddorders;|Inventory|istheabsoluteaccumulatedinventorydividedbythetradingvolume(innumberofshares),reportedasthemedianacrossmembersineachgroup;q/tistheaggregatenumberofaddordersdividedbytheaggregatenumberofexecutions;Minimumlatency(Limitorderduration)istheminimum(median)lifetimeoflimitordersthatarecanceledbeforetheendofthetradingday,reportedinmillisecondsandasthemedianacrossmembers in eachgroup;Liquidity supply is the fractionof the group’s tradingvolumewhere themember is on thepassive side, counted in either SEK tradingvolume or number of trades. The last three rows contain p‐values from a t‐test for equality betweenmeans, aWilcoxon rank sum z‐test for equality betweenmediansandanF‐testforequalitybetweenvariancesacrossthetwogroups.

51

Table5:HFTmarketmakerandopportunistictraderactivityacrosssubsetsofstocks

PanelA:Marketmakeractivity

Subsettingvariable

Marketmakerpresence

Fractionofalltradingvolume

Fractionofalladdorders

|Inventory| q/tMinimumlatency(ms)

Limitorderduration(ms)

Liquiditysupply(volume)

Liquiditysupply(trades)

Marketcapitalization(SEK)

10highest 0.665 0.679 0.81 0.05 11.06 0.08 4481 0.69 0.6710lowest 0.510 0.648 0.86 0.07 13.67 0.12 8593 0.73 0.72z‐test 0.000 0.007 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Tradingvolume(SEK)

10highest 0.672 0.684 0.82 0.05 10.57 0.08 4280 0.69 0.6710lowest 0.545 0.615 0.82 0.08 15.25 0.12 9012 0.71 0.71z‐test 0.000 0.000 0.394 0.000 0.000 0.000 0.000 0.000 0.000

Realizedvolatility

10highest 0.563 0.671 0.83 0.05 12.77 0.09 5921 0.72 0.7110lowest 0.692 0.673 0.84 0.06 11.70 0.11 6953 0.70 0.68z‐test 0.000 0.708 0.993 0.086 0.015 0.000 0.155 0.000 0.000

Relativebid‐askspread

10highest 0.675 0.669 0.85 0.06 12.15 0.11 7487 0.70 0.6810lowest 0.563 0.644 0.82 0.05 13.69 0.09 4771 0.71 0.69z‐test 0.000 0.196 0.006 0.006 0.000 0.000 0.000 0.026 0.146

52

PanelB:Opportunistictraderactivity

Subsettingvariable

Marketmakerpresence

Fractionofalltradingvolume

Fractionofalladdorders

|Inventory| q/tMinimumlatency(ms)

Limitorderduration(ms)

Liquiditysupply(volume)

Liquiditysupply(trades)

Marketcapitalization(SEK)

10highest 0.004 0.321 0.19 0.43 6.25 0.43 3725 0.39 0.3910lowest 0.001 0.352 0.14 0.39 4.80 0.99 1778 0.29 0.23z‐test 0.000 0.007 0.000 0.014 0.000 0.000 0.000 0.000 0.000

Tradingvolume(SEK)

10highest 0.005 0.316 0.18 0.42 6.13 0.45 3791 0.39 0.3810lowest 0.002 0.385 0.18 0.41 5.92 1.14 1727 0.31 0.25z‐test 0.000 0.000 0.394 0.922 0.699 0.000 0.000 0.000 0.000

Realizedvolatility

10highest 0.002 0.329 0.17 0.39 5.91 0.58 1896 0.29 0.2510lowest 0.002 0.327 0.16 0.46 5.05 1.61 4366 0.33 0.29z‐test 0.781 0.708 0.993 0.000 0.047 0.000 0.000 0.000 0.000

Relativebid‐askspread

10highest 0.003 0.331 0.15 0.43 5.18 0.67 2221 0.32 0.2510lowest 0.003 0.356 0.18 0.40 6.16 0.45 2925 0.35 0.33z‐test 0.667 0.196 0.006 0.018 0.008 0.000 0.021 0.003 0.000

53

Table5presentsmediansofthetradingandquotingactivityofmarketmakersinsubsetsofOMXS30indexstocks.EachmedianisestimatedacrossdaysinAugust2011andFebruary2012,andstocks.Thesubsettingvariablesaremarketcapitalization(SEK),tradingvolume(SEK),realizedvolatility,andrelativebid‐askspread.Stocksaresortedexposteachmonthandmediansarereportedforstockswiththe10highestandthe10lowestvaluesofeachsubsettingvariable.Thestock‐dayobservationsarecalculatedasfollows:Marketmakerpresenceisthefractionof10second‐periodsinthecontinuoustradingdaythatamemberhasquotesateitherofthebestbidandofferprices,reportedasthemeanacrossmembersineachgroup;theFractionofHFTtradingvolume isthesumofSEKvolumetradedforallmembersineachgroup(MarketmakersandHFTthatarenotmarketmakers),dividedbythesumofallHFTSEKtradingvolume;theFractionofHFTaddordersisthesumofaddorderspostedbymembersineachgroup,dividedbythetotalnumberofHFTaddorders;|Inventory|istheabsoluteaccumulatedinventorydividedby the tradingvolume(innumberofshares), reportedas themedianacrossmembers ineachgroup;q/t is theaggregatenumberofaddordersdividedby theaggregatenumberofexecutions;Minimumlatency(Limitorderduration)istheminimum(median)lifetimeoflimitordersthatarecanceledbeforetheendofthetradingday,reported inmillisecondsandasthemedianacrossmembers ineachgroup;Liquiditysupply isthe fractionof thegroup’stradingvolumewherethememberisonthepassiveside,countedineitherSEKtradingvolumeornumberoftrades.Foreachsubsettingvariable,p‐valuesfromaWilcoxonranksumz‐testforequalitybetweenmediansarereported.

54

Table6:HFTactivitiesfollowingticksizechanges

Tradingvolume Numberofexecutions Limitorders

Total Active Passive Total Active Passive Additions Cancellations

Downtick Non‐MM(2010) 0.575 0.736 0.342 0.874 0.980 0.026 0.495 0.260 (0.0080) (0.0045) (0.0649) (0.0121) (0.0029) (0.2184) (0.0121) (0.0179)

Non‐MM(2011) 0.528 1.181 0.005 0.343 1.022 0.065 0.007 ‐0.027 (0.0002) (0.0000) (0.3195) (0.0004) (0.0001) (0.1965) (0.6865) (0.2473)

MM(2011) ‐0.187 ‐0.196 ‐0.144 ‐0.206 ‐0.121 ‐0.158 0.046 0.050 (0.0123) (0.1874) (0.0123) (0.0106) (0.5184) (0.0314) (0.2699) (0.2473)

Uptick Non‐MM(2010) ‐0.436 ‐0.486 ‐0.119 ‐0.450 ‐0.474 ‐0.184 ‐0.289 ‐0.282

(0.0006) (0.0006) (0.8087) (0.0004) (0.0003) (0.5360) (0.0009) (0.0066)

Non‐MM(2011) ‐0.440 ‐0.535 ‐0.241 ‐0.426 ‐0.493 ‐0.022 0.077 0.263 (0.0003) (0.0000) (0.0853) (0.0005) (0.0003) (0.5590) (0.1832) (0.0321)

MM(2011) 0.130 0.276 0.042 0.113 0.097 0.062 ‐0.062 ‐0.072 (0.0742) (0.0597) (0.2840) (0.1627) (0.1197) (0.3986) (0.5590) (0.4359)

Table6containsmedians forabnormaleffectsof ticksizechangesonHFTactivities: tradingvolume(Volume),executed transactions(Executions),additionsoforders(Additions),andcancellationsoforders(Cancellations)acrossevents.EachabnormaleffectisdefinedasHFTactivityrelativethesumofallHFTsandnon‐HFTsactivity, inastock thatexperiencesa tick sizechangeevent (Treatment),after relative tobefore theevent, relative theaveragecorresponding fractionofactivityinnon‐eventstocks(Benchmark).Forexample,abnormalmarket‐makingHFTtradingvolumeduringeventiis:

% ,

% ,

% ,

% ,

where , , / , , , % , , / , , , , ( , ) is themarket‐makingHFTvolumebefore (after)

theevent, , , isallHFTvolumebefore after theevent,and , ( , )isthenon‐HFTvolumebefore(after)theevent.Thetablereportsthemedianabnormal activity across events, differentiatingbetweenup‐ anddown‐tick size changes. Eachmedian effect is testedusing ap‐value fromaWilcoxonsigned rank test, displayed in parenthesis. Active (passive) trading volume andnumber of executed transactions refers to the casewhenHFTs are initiating amarketorderormarketablelimitorder(areexperiencinganexecutionoftheirstandinglimitorder).

55

Table7:Intradayrealizedstockreturnvolatilityfollowingticksizechanges

1min 5min 10min 15min

Downtick 2010 ‐0.292 ‐0.300 ‐0.225 ‐0.164 (0.1422) (0.2977) (0.4210) (0.4777)

2011 ‐0.113 0.275 0.437 0.128 (0.8303) (0.1065) (0.0574) (0.2472)

Uptick 2010 0.115 0.156 0.137 0.168

(0.0238) (0.0255) (0.0293) (0.0335)

2011 0.003 ‐0.040 ‐0.044 0.075 (0.5590) (0.7210) (0.8202) (0.5590)

Table 7 contains medians for abnormal effects of tick size changes on intraday realized stock returnvolatility, usingmidpoint quote changes in 1, 5, 10, and 15minute intervals. Each abnormal volatilityeffect isdefinedas the volatilityof a stock that experiences a tick size changeevent (Treatment), afterrelativetobeforetheevent,relativetheaveragecorrespondingvolatilityofnon‐eventstocks(Benchmark)accordingto:

,

,

,

,

where , , is the volatility after (before) the event. The table reportsmedian abnormal volatilityacrossevents,differentiatingbetweenup‐anddown‐ticksizechanges.Eachmedianeffectistestedusingap‐valuefromaWilcoxonsignedranktest,displayedinparenthesis.

56

Appendix:SummarystatisticsforeachOMXS30indexstock–August,2011

ISINCodeExchangecode

Marketcap

(MSEK)

Tradingvolume

Numberoftrades(x1000)

Numberofsharestraded

Tradesize DepthNominalbid‐askspread

Relativebid‐askspread

Volatility

CH0012221716 ABB 69820 507 5864 3733 86449 16447 14.53 0.107 0.143

SE0000695876 ALFA 52222 390 5922 3330 65832 6360 14.36 0.122 0.225

SE0000255648 ASSAB 51489 311 4447 2180 69908 6921 15.60 0.110 0.180

SE0000101032 ATCOA 120201 968 12363 7250 78294 13869 13.32 0.099 0.256

SE0000122467 ATCOB 49753 265 4817 2234 54933 8151 13.96 0.117 0.302

GB0009895292 AZN 70376 605 6757 2099 89502 13520 17.92 0.062 0.058

SE0000869646 BOL 23919 404 9502 4767 42542 5938 7.49 0.088 0.312

SE0000103814 ELUXB 32015 401 6619 3673 60614 7430 13.50 0.124 0.295

SE0000108656 ERICB 215480 1388 15536 19488 89349 18218 6.29 0.089 0.167

SE0000202624 GETIB 36404 159 2820 1048 56327 4984 23.01 0.151 0.177

SE0000106270 HMB 288629 1338 13651 6819 98001 17702 13.51 0.069 0.121

SE0000107419 INVEB 56936 389 5700 3216 68321 8882 13.51 0.111 0.153

SE0000825820 LUPE 30615 249 6116 2998 40768 3862 10.19 0.122 0.329

SE0000412371 MTGB 19921 137 3205 399 42722 3448 44.93 0.131 0.235

SE0000427361 NDASEK 236765 873 8993 15101 97055 17274 6.53 0.113 0.180

FI0009000681 NOKISEK 2599 90 2661 2533 34005 5965 3.38 0.095 0.316

SE0000667891 SAND 100479 808 11728 9798 68877 9189 8.04 0.097 0.280

SE0000112724 SCAB 52028 299 5251 3561 56916 6081 7.60 0.090 0.141

SE0000308280 SCVB 46000 284 5698 2655 49875 5638 14.84 0.138 0.279

SE0000148884 SEBA 81593 450 10482 11619 42937 3831 2.96 0.076 0.251

SE0000163594 SECUB 20405 126 3036 2348 41439 3118 9.47 0.176 0.222

SE0000193120 SHBA 106726 519 7249 2947 71571 8078 14.22 0.081 0.134

SE0000113250 SKAB 38493 282 5649 3066 49887 6000 8.22 0.089 0.187

SE0000108227 SKFB 61638 651 8291 4500 78475 10378 14.24 0.099 0.240

SE0000171100 SSABA 15421 174 5173 2666 33594 3568 8.04 0.124 0.267

SE0000242455 SWEDA 83090 793 11229 8934 70594 10427 7.70 0.086 0.352

SE0000310336 SWMA 49011 302 4700 1339 64244 7823 21.30 0.094 0.105

SE0000314312 TEL2B 56629 375 5468 3026 68664 8713 13.57 0.109 0.149

SE0000667925 TLSN 196283 536 8504 12236 62969 9200 2.81 0.064 0.105

SE0000115446 VOLVB 115269 1727 23913 21694 72223 19473 6.75 0.085 0.280

57

Appendix:SummarystatisticsforeachOMXS30indexstock–February,2012

ISINCodeExchangecode

Marketcap

(MSEK)

Tradingvolume

Numberoftrades(x1000)

Numberofsharestraded

Tradesize DepthNominalbid‐askspread

Relativebid‐askspread

Volatility

CH0012221716 ABB 68276 257 3032 1824 84828 18299 13.22 0.093 0.026

SE0000695876 ALFA 57004 321 4548 2379 70590 12825 13.41 0.099 0.039

SE0000255648 ASSAB 70164 244 3688 1254 66209 10406 15.49 0.080 0.044

SE0000101032 ATCOA 144879 527 5337 3124 98828 23204 13.03 0.077 0.033

SE0000122467 ATCOB 59860 152 2639 1012 57610 10069 15.83 0.105 0.036

GB0009895292 AZN 57422 340 3762 1101 90245 16264 15.67 0.051 0.016

SE0000869646 BOL 31700 339 5738 2859 59064 14120 13.25 0.112 0.065

SE0000103814 ELUXB 43693 476 6751 3421 70573 14458 14.54 0.103 0.080

SE0000108656 ERICB 200121 773 8921 11951 86663 25991 5.41 0.084 0.035

SE0000202624 GETIB 42053 123 2439 651 50350 7080 15.53 0.082 0.020

SE0000106270 HMB 347348 681 6847 2907 99518 28272 12.52 0.053 0.018

SE0000107419 INVEB 67093 229 3279 1597 69824 14337 12.90 0.090 0.022

SE0000825820 LUPE 49085 242 4470 1530 54086 10792 14.71 0.093 0.054

SE0000412371 MTGB 19708 128 2983 376 42749 4390 32.31 0.096 0.056

SE0000427361 NDASEK 258216 586 6753 9458 86825 24864 5.88 0.095 0.047

FI0009000681 NOKISEK 2346 131 4264 3723 30812 5603 2.41 0.069 0.067

SE0000667891 SAND 126818 646 7111 6317 90871 21485 10.97 0.107 0.058

SE0000112724 SCAB 71992 223 3428 1941 64953 11574 11.63 0.101 0.026

SE0000308280 SCVB 53040 251 4153 2024 60527 9720 15.03 0.122 0.078

SE0000148884 SEBA 107438 424 8252 8845 51361 9057 2.79 0.057 0.056

SE0000163594 SECUB 22127 175 3702 2807 47268 4759 6.68 0.106 0.090

SE0000193120 SHBA 136127 391 5198 1814 75206 12616 14.64 0.068 0.032

SE0000113250 SKAB 48272 205 3331 1695 61563 12961 12.62 0.104 0.029

SE0000108227 SKFB 69028 393 5019 2350 78234 16064 14.26 0.085 0.045

SE0000171100 SSABA 16697 255 6201 3591 41102 6934 7.02 0.098 0.077

SE0000242455 SWEDA 109260 475 5576 4388 85261 16868 11.55 0.107 0.050

SE0000310336 SWMA 53825 179 3183 731 56123 8199 16.62 0.068 0.027

SE0000314312 TEL2B 57079 276 4173 2107 66036 13977 11.40 0.087 0.024

SE0000667925 TLSN 209056 396 7036 8451 56302 16362 1.61 0.034 0.017

SE0000115446 VOLVB 141439 1004 12173 10631 82489 25885 6.19 0.065 0.044