How Algorithms Interact: Goffman’s ‘Interaction Order’ in Automated

Trading

Donald MacKenzie

April 2016

Author’s Address: School of Social and Political Science University of Edinburgh Chrystal Macmillan Building Edinburgh EH8 9LD Scotland DonaldMacKenziePA@ed.ac.uk

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HowAlgorithmsInteract:Goffman’s‘InteractionOrder’inAutomatedTrading

Abstract

Inatalkin2013,KarinKnorrCetinareferredto‘theinteractionorderofalgorithms’,a

phrasethatimplicitlyinvokesErvingGoffman’s‘interactionorder’.Thispaperexplores

theapplicationofthelatternotiontotheinteractionofautomated-tradingalgorithms,

viewingalgorithmsasmaterialentities(programsrunningonphysicalmachines)and

conceivingoftheinteractionorderofalgorithmsastheensembleoftheireffectsoneach

other.Thepaperidentifiesthemainwayinwhichtradingalgorithmsinteract(via

electronic‘orderbooks’,whichalgorithmsboth‘observe’andpopulate)andfocuseson

twoparticularlyGoffmanesqueaspectsofalgorithmicinteraction:queuingand

‘spoofing’,ordeliberatedeception.FollowingGoffman’sinjunctionnottoignorethe

influenceoninteractionofmattersexternaltoit,thepaperexaminessomeprominent

suchmatters.Empirically,thepaperdrawsondocumentaryanalysisand185interviews

conductedbytheauthorwithhigh-frequencytradersandothersinvolvedinautomated

trading.

KeyWords

Interactionorder;ErvingGoffman;KarinKnorrCetina;algorithm;high-frequency

trading;queuing;spoofing.

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‘[H]umanawarenesscomprisesthetipofahugepyramidofdataflows,mostof

whichoccurbetweenmachines’(Hayles,2006:165).

AsHaylespointsout,humanbeingsareincreasinglyenmeshedina‘cognisphere’,

sharedwithmachines,inwhichmanyimportantprocessestakeplaceamongthose

machines,withoutdirecthumaninvolvement.HowshouldwhatBeer(2009:987)calls

‘thetechnologicalchallengestohumanagencyofferedbythedecision-makingpowersof

establishedandemergentsoftwarealgorithms’betheorised?Thispaperaddressesthis

questionforonespecificarea:automatedfinancialtrading,especiallyhigh-frequency

tradingorHFT,whichisultrafastandinvolvesverylargenumbersoftrades.

ThepapertakesupasuggestionmadebyKarinKnorrCetinainatalktothepanel

‘TheorizingNumbers’attheAmericanSociologicalAssociation,inwhichsheusedthe

evocativephrase:‘theinteractionorderofalgorithms’(KnorrCetina,2013).Itpointsus

inasomewhatdifferentdirectiontomuchrecentworkonalgorithms,whichdraws

upontheoristsassophisticatedandwell-knownasHaylesherself(e.g.1999,2012;see

alsoGane,VennandHand,2007),Foucault(e.g.Cheney-Lippold,2011andBucher,

2012),Deleuze(e.g.1992:seee.g.,SavatandPoster,2005andCheney-Lippold,2011),

Latour(e.g.2005)andLash(2002,2007;seeBeer,2009).

Theterm,‘theinteractionorder’,wascoinedbyErvingGoffman,whoseprimary

reputationisnotasatheorist–evenacriticassympatheticasBurns(1992)couldfind

histheorisingunsystematicandsometimesevencareless–butasahugelyinsightful

observerofsocialinteraction.‘TheInteractionOrder’wasthetitleofGoffman’s

intended1982PresidentialAddresstotheAmericanSociologicalAssociation,

undeliveredbecausehewasalreadysufferingfromthecancerthatwassoontokillhim,

butpublishedthefollowingyear(Goffman,1983).Init,helaidoutwhathesawasmost

centraltohislife’swork:

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Socialinteractioncanbeidentifiednarrowlyasthatwhichuniquelytranspiresin

socialsituations,thatis,environmentsinwhichtwoormoreindividualsare

physicallyinoneanother’sresponsepresence.(Presumablythetelephoneandthe

mailsprovidereducedversionsoftheprimordialrealthing.)…Myconcernover

theyearshasbeentopromoteacceptanceofthisface-to-facedomainasan

analyticallyviableone–adomainwhichmightbetitled,forwantofanyhappy

name,theinteractionorder(Goffman,1983:p.2,emphasisinoriginal).

Theuneasyparenthesisinthatquotationpointstotheneedtoquestionthe

primaryroleofphysicalco-presenceinGoffman’sconceptionoftheinteractionorder.In

thedecadessince1983,‘thetelephoneandthemails’havebeenjoinedbymultipleother

formsofmediatedcommunication:electronicmail,textmessagesandotherformsof

instantmessaging,socialmedia,Skypeandotherformsoftelepresence,etc.Asthese

havegrowninimportance,KnorrCetinaissurelyrighttosuggestsupplementing

Goffman’sfocusonspatialproximitywithabroader,temporalnotionof‘response

presence’asaccountability‘forrespondingwithoutinappropriatedelaytoanincoming

attentionorinteractionrequest’(KnorrCetina,2009:74).

Giventhispaper’sfocusonalgorithmictrading,itisparticularlyrelevantthat

bothKnorrCetinaherselfandAlexPredahaveproductivelydeployedreworked

versionsofGoffman’s‘interactionorder’toanalysehumanbeingstradingelectronically.

MuchofKnorrCetina’sresearchonfinancialmarketshasconcernedforeign-exchange

dealersinbanktradingroomscommunicatingwithothertraders(indifferentbanks,

butpersonallyidentifiableandsometimespersonallyknown)viatheReuters

‘conversationaldealing’system,anearlyelectronicsystem‒stillinusetoday‒that

combinesautomatedrequestsforpricequotationswiththecapacitytoformulateTelex-

stylemessagesconveyingup-to-datemarketinformation,pleasantries(‘please’,

‘thanks’),andthedetailsneededtosettletrades(see,especially,KnorrCetinaand

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Bruegger,2002a).However,KnorrCetinaalsoexamineshumantradersinteractingwith

afullyanonymouselectronicmarket(e.g.KnorrCetina,2009:72-73),asdoesPreda

(2009and2013).IntheworkofKnorrCetinaandPreda,Goffman’snotionofthe

interactionordergetsstretchedbeyondtemporalresponsepresenceamongspatially

separatebutidentifiablehumans,as‘themarket’itselfbecomesapartyto‘postsocial’

interaction(KnorrCetinaandBruegger,2002b).AsKnorrCetinapointsout,in

projecting‘themarket’,traders’computerscreensproject‘an“other”forparticipants,

withwhomtheseparticipantsinteract’(KnorrCetina,2009:73;seealsoKnorrCetina

andPreda,2007).Predadiscovershumantraders–nolongerintradingrooms,but

oftenphysicallyentirelyalone–tryingtodisaggregate‘themarket’intodifferentkinds

ofagent(forexample,‘anindividual[human]trader,aninstitution,orarobot’:i.e.,a

tradingalgorithm)thatdodifferentthings,andsometimes(eventhoughalone)audibly

addressingtheseabsent,imagined,unhearingothers,‘engagingwith“guys”,“dudes”,

and“buds”’(Preda,2013:42;Preda,2009:687).

KnorrCetina’sinvocationof‘theinteractionorderofalgorithms’invitesusto

takeyetafurtherstep,whichisthispaper’sfocus:toextendthenotionof‘interaction

order’tosituationsinwhichtradingalgorithmsinteractwitheachotherratherthan

withhumanbeings.First,though,weneedtobeclearwhat‘algorithm’meansinthis

context,andwhatitmightmeanforalgorithmstointeract.Ifollowhowmy

intervieweesusetheterm‘algorithm’.Forthem,algorithmsarenotsimplytheabstract

‘effectiveprocedures’(finitesetsofexact,‘mechanical’instructions)of

metamathematicsorcomputerscience.Rather,an‘algorithm’isamaterial

implementationofsuchaprocedure:i.e.,acomputerprogramrunningonaphysical

machine.

Althoughthisviewofalgorithmsisimplicitinmuchoftheliteraturepointedto

above–forexample,inLash’sdiscussionof‘[p]owerthroughthealgorithm’(2007:71)

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–itisworthspellingoutexplicitlythatanalgorithmisamaterialentitythatdoesthings

materially:ultimately,electrically.(Theneedforspeedinautomatedtradingmeansthat

thereisasenseinwhichthoseinvolvedinithavetobematerialists.Forexample,they

cannotsuccessfullyconceiveofcomputersasabstractmachines,buthavetothinkof

themasassemblagesofmetal,plasticandsiliconthroughwhichelectricalsignalspass:

seeMacKenzie[2014a].Thispointstotherelevancehereoftheoreticaltraditionsin

whichmaterialityisprominent,suchas‘mediamaterialism’[e.g.,Kittler,2006;Parikka,

2015].)Amongthethingsanalgorithmdoes,inautomatedtrading,istohavematerial

effectsonthebehaviourofotheralgorithms;reciprocally,theirbehaviourinfluences

whatitdoes.TheensembleofsucheffectsiswhatImeanbythe‘interactionorderof

algorithms’.

Goffmanwasathorough-going,albeittacit,materialist.Humanbodies,their

positioning,theirphysicalsettings,theirgestures,glances,blushes,etc,areprominentin

hiswork:see,e.g.,Goffman(1959,1963,1967and1968).Thereader’sintuitionsmay,

however,rebelagainsttheapplicationofGoffman’s‘interactionorder’tothemutual

effectsofalgorithms.Their‘siliconbodies’differradicallyfromhumanflesh,andthey

interactexplicitlyandinstrumentally,notsubtlyandexpressivelyashumansdo.And,of

course,asfarasweknow,tradingalgorithmshavenoself-consciousness,whilehumans

areoftenpainfullyself-aware.

Intuitionsneverthelessneedtobeinterrogated.ThesuccesswithwhichKnorr

CetinaandPredahaveappliedtheirextendedconceptualisationsofthe‘interaction

order’tohumanbeingstradingelectronicallyandanonymouslysuggeststhatweshould

notrejectapriorithenotion’sapplicationtotradingbyalgorithms.Afterall,the

informationandformsofactionavailabletohumanbeingsinmostoftoday’s

anonymouselectronicmarketsareoftennodifferentfromthoseavailabletoalgorithms.

Bothhumansandalgorithmsfacemuchthesametasks(especiallythetaskofdrawing

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inferencesfromthe‘orderbooks’describedinthispaper’ssecondsection)andtheyact

inthesameway,byentering,cancelling,orsometimesmodifyingorders,eveniftheydo

itwithdifferenttools:humansusingvisualinterfaces,keyboardandmouse;algorithms

employingdirect,computer-to-computercommunication.

Thispaperthereforeasksthereadertosuspendintuitivejudgementwhileit

followsKnorrCetina’spointerandexperimentswithapplyingGoffman’s‘interaction

order’toautomatedtrading.Theempiricalmaterialdrawnonisresearchbytheauthor

onautomatedtrading(especiallyonhigh-frequencytrading,butalso,forexample,on

the‘executionalgorithms’usedbyinstitutionalinvestorstosplitupbigorders),onthe

exchangesandothertradingvenuesonwhichittakesplace,andonitstechnological

underpinnings.Intotal,185interviewshavebeenconducted,mainlyinChicagoand

NewYork,withthedevelopersoftradingalgorithms,thetraderswhousethem(who

areoftenthesamepeople),exchangestaff,providersoftechnologicalservices,

regulators,etc.Theseinterviews(whichcoveredboththecurrentpracticesof

automatedtradingbutalso‒whentheintervieweehadhadalongenoughcareerto

havefirst-handexperienceofthis‒thehistoricalprocessesthathaveshapedcurrent

practices)havebeensupplementedbyparticipantobservationattwoindustry

meetings,avisittoCermak(adatacentreinChicagothathousesmuchalgorithmic

trading),andexaminationofweb-baseddiscussionforums,ofthetechnicalliterature,of

tradepress,ofenforcementactionsbyregulators,etc.

Fivesectionsfollowthisintroduction.Thefirstsetsthestagebydrawingonthis

empiricalresearchtodescribethephysicalsettingswithinwhichtradingalgorithms

interactandtoidentifythemostimportantwayinwhichtheydoso.Nextcomesa

sectiononaformofinteractiondiscussedinGoffman’sPresidentialAddress(andalso

prominentinethnomethodologicalanalysessuchasLivingstone,1987)thatisofhuge

importanceinautomatedtrading:queuing.Thenfollowsadiscussionofoneof

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Goffman’smostpersistentconcerns:dissimulation,includingaformofitparticularly

salientforautomatedtrading,‘spoofing’.Thatsectionincludesadiscussionofa

fascinatingepisodeinwhichalgorithmicactionatoddswith‘normal’behaviourin

queueshasformedthebasisofanaccusationofspoofing.Thepaper’spenultimate

sectiontakesupGoffman’sremindernottoneglect‘thedependencyofinteractional

activityonmattersoutsidetheinteraction’(Goffman,1983:12)byexaminingsomeof

themostimportantofthosemattersastheybearuponalgorithmictrading.Thepaper’s

conclusionis,Ihope,appropriatelymodest:itarguesthatGoffman’s‘interactionorder’

pointsusintherightdirectionwhenstudyingtradingalgorithms,butitalsoidentifies

themethodologicaldifficultyofresearchonhowtradingalgorithmsinteract.

HowTradingAlgorithmsInteract

Asalreadyemphasised,thispaperviewstradingalgorithmsmaterially,asprograms

runningontradingfirms’computerservers.Many,perhapsmost,ofthoseserversareto

befoundinnomorethanfifteencomputerdatacentresworldwide,ineachofwhich

thousandsoftradingalgorithmsmayberunningatanyonetime.Someofthesecentres

areownedbyexchangessuchastheNewYorkStockExchange;othersaremulti-user

buildings,suchasChicago’sCermak,NY4inSecaucus,NewJersey,andLD4inSlough.

Cermakusedtobeagiantprintworks(theSearsRoebuckcataloguewasprintedthere:

seeMacKenzie,2014b),butmostothertradingdatacentresarepurpose-built,andeasy

tomistakeforwarehouses.Theycontainfewhumanbeings,mainlysecurityand

maintenancepersonnel.Hugeamountsofenergyflowintodatacentresintheformof

electricity,andflowoutasheatextractedbypowerfulcoolingsystems(tensof

thousandsofcomputerserverspackedclosetogethergeneratealotofheat).Those

serversarehousedonracksinrowsofcages:normallywire-mesh,butsometimeswith

opaquedoorsforprivacy.Abovethecagesisagiantspider’swebofcopperandfibre-

opticcablesthatconnectsserverstoeachother(andcarriesfibre-optic,microwaveand

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satellitesignalsfromtheoutsideworld).Someofthecagescontaintheserversand

switchesthatmakeupthecomputersystemsofexchangesandotherorganisedtrading

venues;othercagescontaintheserversofthefirmstradingonthoseexchanges.The

reasonfortheclusteringintoaremarkablysmallnumberofverybigbuildingsistrading

firms’desiretohavetheirservers‘co-located’:placedascloseaspossibletoexchanges’

systems.

Withlimitedexceptions,thetradingalgorithmsrunningontheseserversdonot

interactdirectlywitheachother,butindirectly,mostcommonlyviaanexchange’s

computersystem,andinparticularviaanelectronicfilecalledtheexchange’s‘central

limit-orderbook’,ormoresimply,its‘orderbook’.(Toavoidclutteringthetext,Ihave

gatheredtogetherthemainexceptionstoitsempiricalgeneralisationsinAppendix1.)A

pictorialrepresentationofatypical–buthypothetical,becauseIwanttouseitto

illustrateavarietyofpointsasclearlyaspossible–orderbookisinFigure1.Itisan

orderbookforshares,but(withexceptionsbrieflydescribedinAppendix1)thetrading

offutures,foreignexchange,U.S.Treasurybondsandstockoptionsissimilarinform.On

theleft-handsideofFigure1arethebidstobuythesharesinquestion:forexample,

thereisabidtobuy100sharesat$44.99;abidtobuy44shares,alsoat$44.99,etc.On

theright-handsidearetheofferstosell,forexampleanoffertosell100sharesat

$45.00.

NohumantradersaretobefoundindatacentressuchasCermak:humansarein

thatsenseontheperipheryoftoday’strading.Atradingalgorithmthatishousedina

datacentreentersbidsoroffersintotheorderbook(orcancels,orsometimesmodifies,

bidsoroffersithaspreviouslyentered)byinstructingthenetworkinterfacecardofthe

computerserveronwhichitisrunningtosendanelectronicmessagethroughthecable

–typicallyoftheorderof100metreslong–thatthreadsitswaythroughthespider’s

webandconnectstheservertotheexchange’scomputersystem.Thatsystemcontains

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programscalled‘matchingengines’,whichprocesstheseincomingmessagesandupdate

theorderbooksforthesharesorotherfinancialinstrumentsbeingtraded.Ifamatching

enginefindsa‘match’(abidtobuyafinancialinstrument,andanoffertosellit,bothat

thesameprice)itexecutesatrade;otherwise,itsimplyaddsnewbidsandofferstothe

orderbook.

Aswellastradingalgorithmssendinginthebidsandoffersthatpopulatethe

orderbook,theyalso‘observe’it(myterm,notinterviewees’).Wheneveramatching

enginereceivesaneworderoracancellationormodificationofanexistingorder,orit

findsamatch,itsendstheexchange’sfeedserveramessagecontainingtheanonymised

details.Thatserverthendisseminatesthesemessagestosubscriberstotheexchange’s

datafeed.(The‘hiddenorders’mentionedinAppendix2are,however,not

disseminated.)Thedatafeedflows–againthrougharound100metresoffibre-optic

cable–totradingfirms’servers,whichusethestreamofmessagestoconstructtheir

ownelectronic‘mirrors’oftheorderbook.

Tradingalgorithmsinterrogatethismirroredorderbookinavarietyofways,

seekingtopredictpricechanges.IntheorderbookinFigure1,forexample,thereare

offerstosell4,240shares,andbidstobuy1,324;‘supply’thusexceeds‘demand’,and

thusafallinpricemightbepredicted.Whilenosophisticatedtradingalgorithmwould

relyonacalculationassimplisticasthis,intervieweesreportedheavyrelianceby

algorithmsonvariousformsofweightedaverageofthenumbersoffinancial

instrumentsbeingbidforandofferedatdifferentprices,alongwithavarietyofwaysof

inferringthedynamicsofhowtheorderbookischangingthroughtime.Thepervasive

concern,discussedbelow,with‘spoofing’meansthatsophisticatedtradingalgorithms

willalsodeployvariousmeansofassessingthelikelihoodthattheexistingbidsand

offersintheorderbookwillactuallybecancelledbeforetheyareexecuted,andwill

discountthoseforwhichthisisthecase.

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Predictionsbasedonthesealgorithmic‘observations’oftheorderbook(along

withsimilarobservationsoftheorderbooksforotherinstrumentswhosepricesare

knowntobecorrelatedwiththoseoftheinstrumentbeingtraded)areusedfortwo

mainformsofprofit-seekingtrading.Theconceptuallysimpleris‘liquidity-taking’or

‘aggressive’trading.Supposeanalgorithm’sobservationsgeneratetheinferencethat

thepriceofthesharesbeingtradedviatheorderbookinFigure1isabouttofall.It

couldthensendtothematchingengineanordertosellsharesat$44.99,whichthe

matchingenginecanexecuteatleastinpartassoonasithasprocessedit,becauseitcan

matchitwithexistingbidstobuyat$44.99.(Thatiswhyitwouldbecalleda‘liquidity-

taking’order:itremovesanexistingorderorordersfromtheorderbook.)Iftheprice

doesindeedfallbelow$44.99,thenthealgorithmcanbuybacksharesataprofit.

Liquidity-making,incontrast,involvesanalgorithmsendingthematching

engineordersthatcannotimmediatelybeexecuted,anditsmostsystematicform

(knownas‘market-making’)involvescontinuallykeepingbothabidandahigher-priced

offerintheorderbook,inthehopethatbothwillbeexecutedandthedifferenceintheir

pricescapturedasprofit.Suppose,forexample,thatinFigure1thesamealgorithmhas

enteredintotheorderbookboththebidtobuy100sharesat$44.99andtheoffertosell

100sharesat$45.00.Ifbothareexecuted,thealgorithmwillmakeaprofitofonecent

foreachsharetraded.Thatsoundsnegligible,buthigh-frequencytradinginvolvesthe

buyingandsellingofhugenumbersofshares,sotinyprofitsaddup.

Amarket-makingalgorithmhasjustasmuchneedasaliquidity-taking

algorithmelectronicallyto‘observe’thecontentsoftheorderbookandthustopredict

pricemovements,becauseifpricesmovesharplyitcaneasilybeleftwithaninventory

ofsharesthepricesofwhichhavefallen,orwithwhatparticipantscalla‘shortposition’

inshareswhosepriceshaverisen.1Theconstantobservationoftheorderbookby

1Thatistosay,itmayhavesoldsharesthatitsfirmdoesnotown.

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tradingalgorithmsofallkinds,andtheactionstheyfrequentlytakeinresponsetothat

observation,meanthatanexplicit‘global’electronicrepresentation–arepresentation

oftheentiretyof‘themarket’inquestion–playsamuchlargerrolethaninmost

ordinaryhumansocialinteraction.(Ataparty,forexample,mostparticipants’attention

isdevotedtoasmallsubsetofwhatisgoingon,withonlyananxioushostorhostess

maybemonitoringtheeventasawhole:see,e.g.,Goffman1963.)

AsYuvalMillopointedouttomeinapersonalcommunication,thecrucialroleof

aglobalrepresentationinalgorithmictradingsuggeststheneedfornuance,when

analysingit,ininvokingmetaphors–suchas‘swarms’(seeVehlken,2013)–inwhich

thereisself-organisationresultingfromlocalinteractions,forexamplebetweennearest

neighbours.(Therearesomelocalinteractionsamongtradingalgorithms:seeAppendix

1.)Again,though,thecentralroleofaglobalrepresentationisfullyconsistentwith

KnorrCetina’sandPreda’sextensionsofGoffman’s‘interactionorder’.Thehuman

traderstheystudiedalsodevotemuchorsometimesevenalloftheirattentiontoa

globalrepresentationonscreenoftheoverallmarket,arepresentationthattodayis

usuallysimplyacomputerfilepresentedinaform(suchasFigure1)suitedtohuman

eyes.Likealgorithms,thosehumantradersalsosimultaneouslyobserveandconstruct

theobjectoftheirattention.

Queuing

Aftersketchingoverallfeaturesofthehumaninteractionorder,Goffman(1983:6)went

on‘totrytoidentifythebasicsubstantiveunits,therecurrentstructuresandtheir

attendantprocesses’asking‘[w]hatsortofanimalsaretobefoundintheinteractional

zoo?’Amonghisfirstexampleswasthequeue:‘[w]hatqueuesprotectisordinalposition

determined“locally”byfirstcomefirstplaced’(Goffman1983:16).

Thatorderingispreciselytheoneenforcedbymostmatchingengines(forthe

mainexceptions,seeAppendix1).Forexample,theoffertosell50sharesat$45.00in

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Figure1willbeexecutedonlyoncetheearlieroffertosell100isexecutedorcancelled.

Itisnaturaltoconceptualisethisorderingasa‘queue’,andthatishowparticipantsdo

indeedthinkofit.Queuesareofhugeimportanceinautomatedtrading;Pardo-Guerra

(forthcoming)summarisesthefield’shistoryas‘from[trading-floor]crowdstoqueues’.

AsthoseofmyintervieweeswhohadtradedmanuallyinChicago’stradingpits

reported,anorderingsimilartothestartofaqueuedidoftenemergeinthosecrowds.

Inapit,bidsandofferswereeithershoutedoutorhand-signalled,andwerethus

observabletothetraderscrowdedintothepit.Whileavarietyoffactors–including

informal‘sharing’normsandreciprocity–affectedwhogotwhichtrade,therewasoften

agreementastowhichtraderhad,forexample,madethefirstbidatagivenprice,and

aninformalconventionthats/hethendeservedtohavethatbidexecutedfirst.This

limitedformoforderingwas,inclassicallyethnomethodologicalfashion,‘reflexive,self-

organizing,organizedentirelyinsitu,locally’(Livingston,1987:10).Inautomated

trading,however,queuesarenotsimplyself-organised:theyarestructured

electronicallybyexchanges’matchingengines.

Queuepositionisnotapressingconcernfor‘aggressive’algorithms(liquidity-

takingordersdon’tusuallyencounterqueues),butitmattersenormouslytomarket-

makingalgorithms’liquidity-makingorders.Iftheseordersaretoofarbackinthe

queue,theymaysimplyneverbeexecuted,andsonoprofitwilleverbemade.Gettingto

thefrontofthequeueisamatteroftechnicalexpertise(suchasthe‘close-to-the-metal’

programming,asparticipantscallit,neededtospeedprocessingbyacomputerasa

physicalmachine)andofspatiallocation.Queuepositionisonechiefreasonwhy

tradingfirmspayexchangestoco-locatetheirserversalongsidetheexchange’s

computersystem.Speed,andthereforequeueposition,can,however,alsobeachieved

moreinformally.Beforetheelectronicmessagescontainingordersreachthematching

engine,theyareprocessedbyordergateways.Thesearenormallyidenticalcomputer

servers,runningidenticalsoftware,andidenticallylinkedtothematchingengine.

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However,eachgatewaytypicallyservesmorethanonetradingfirm,andifafirmhasto

shareagatewaywithafirmwhosealgorithmssendinlargenumbersoforders,the

former’salgorithms’ordersmaybedelayed.Avoidingthiscanbeamajorpractical

issue;itis,forexample,helpful(Iwastoldbyaformerhigh-frequencytrader)toknow

exactlywhomtospeaktoattheexchangeshouldithappen.‘Ifyoudidn’tknowtocall

thatperson,you’llstartatsomelow-levelhelp-centredesk’.

Therearealsoothersubtletiestoalgorithmicqueueing,whichgobeyondthe

needforspeed,andwhicharesometimesdeeplycontroversialamonginsiderstothe

worldofautomatedtrading.AsbothGoffmanandethnomethodologistssuchas

Livingston(1987)emphasised,theinteractionorderofhumanqueuesisamoralorder:

firstcomefirstserved‘producesatemporalorderingthattotallyblockstheinfluenceof

suchdifferentialsocialstatusesandrelationshipsasthecandidatesbringwiththemto

theservicesituation’(Goffman,1983:14).EspeciallyinUSsharetrading,avarietyof

typesofbidsandoffersareavailabletosomealgorithms(butnotalwaystoothers),

whichcanbeusedtohelpanalgorithmgettothefrontofthequeue:seeAppendix2.

Thesebidsandoffershavegeneratedmuchcontroversy(bothamongmyinterviewees

andalsoinpublicforums:see,e.gBodek2013).Theaccusationagainstthemhasin

effectbeenthattheyallow‘differentialsocialstatusesandrelationships’illegitimatelyto

influencequeueposition.

Dissimulation

Asnoted,oneofGoffman’spersistentinterestswastheroleofdissimulationin

interaction.Hewas,ofcourse,nonaivemoralist,andfullyunderstoodthatpresentinga

falseimpressionissometimesentirelyappropriate(itis,forinstance,rightforamedical

studentwhoisnervoustohidethatfactwhentreatingapatient)andthat‘tact’‒for

instance,pretendingnottonoticeanoccurrencethatwouldcauseaparticipanttolose

‘face’‒isoftendesirable.

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Algorithms,too,dissimulate.Considertheexcessofofferstosellintheorder

bookinFigure1.Muchofitismadeupofthreelargeoffers(for1000,400and700

shares)withpricesthatareatleasttwo‘levels’awayfromthebestofferpriceof$45.00.

Undernormalcircumstances,thealgorithm(or,perhaps,evenhumanbeing)thathas

postedthoseofferswillhavethetimetocancelthembeforetheyareexecuted.So

maybetheyhavebeenenteredintotheorderbooksoastoproduceanexcessofoffers

relativetobids,andthuscauseotheralgorithmstopredictapricefallandthereforeto

sell.Theoriginalalgorithmcanthenprofitfromthepricedeclineithascaused,for

examplebybuyingatatemporarilylowprice,cancellingthelargeoffers,andselling

whenpricesrecover?

Foranalgorithmorhumantodothatiswhatmarketparticipantscall‘spoofing’.

Itis,forexample,whatthewestLondontrader,NavinderSinghSarao,whowasarrested

inApril2015,isaccusedofbytheUSDepartmentofJustice.Itsindictmentquotesemails

allegedlysentbyMrSaraoinwhichherequestedtechnicalhelpaddingaparticular

featuretohistradingsoftware,‘acancelifclosefunction,sothatanorderiscancelledif

themarketgetsclose’,withafurtherrefinementtopermithim‘tobeabletoalternate

theclosenessieonepriceawayorthreepricesawayetcetc’(USDepartmentofJustice,

2015:7-8;inFigure1,anoffertosellat$45.01is‘onepriceaway’fromthebestoffer).

Giventhatspoofingisillegitimateandgenerallynowillegal(seebelow),itis

unsurprisingthatnoneofmyintervieweesadmittedtowritingalgorithmsthatspoofed.

Theydid,however,talkabouthowimportantitwasforanyalgorithmthatmadeprice

predictionsonthebasisofananalysisoftheorderbooktobeabletodistinguish‘real’

ordersinthatbookfrom‘spoof’ordersthatwouldbecancelledbeforebeingexecuted.

Oneofthemhad,forexample,programmedhisfirm’salgorithmstogivelessweighttoa

singlebigorderthantomultiplesmallordersofthesameaggregatesize,becausethe

formerwaslesslikelytobe‘real’.Bothheandanotherintervieweewereexperimenting

15

withartificial-intelligencemachinelearningtechniques–especially‘supportvector

machine’techniques–tomaketheiralgorithmsmoresophisticatedinhowthey

distinguished‘real’from‘spoof’orders.(Oneofthesurprisesoftheinterviewswiththe

designersofhigh-frequencytradingalgorithmsistheotherwiseratherlimiteduseof

artificial-intelligencetechniquesinpriceprediction.HFTalgorithms,especiallymarket-

makingalgorithmsthathavetogettotheheadsofqueues,oftenemployconceptually

verysimplebutultrafastinferences,suchas‘weighted’countsofbidsandoffersor

extrapolationtothestockmarketofmovementsinthemarketforstock-indexfutures.

Liquidity-takingalgorithms,whichcanaffordtoactalittlemoreslowly,doemploymore

sophisticatedinferences,butintervieweesatfirmsthatspecialisedinthesealgorithms

reportedthatthepatternsinorder-bookdynamicstheyexploitedwereoftenatthe

borderofstatisticalsignificance,andthelowsignal:noiseratiocauseddifficultiesfor

machine-learningtechniques.)

Whatis,fromtheviewpointofthispaper,aparticularlyinterestingsetof

instancesofallegedspoofingwasdescribedtomebyanintervieweeinJune2015.(It

mayalsobeaconsequentialset.Previousallegationsofspoofinghaveinvolvedeither

individualssuchasSaraoorfirmsmarginaltoautomatedtrading.Theallegedspoofing

intheseinstancesisreportedtobebyamainstreamHFTbusiness.However,noofficial

rulinghasyetbeenmadeonwhethertheaccusationisvalid.)Inalltheprevious

examplesofspoofingIhadencountered,thealleged‘fake’orderswereplacednotatthe

bestbidoroffer,butoneormorelevelsawayfromit.Thenewsetconcernsordersat

thebestbidorofferprice,suchastheoffertosell600sharesat$45.00inFigure1.

Foranalgorithmtoplaceafakeorderatthebestbidorofferpriceispotentially

aneffectivemeansofmovingamarket,becausealgorithmsthatmakeinferencesbased

oncountsofthecontentsoftheorderbooktypically(sointervieweestoldme)‘weight’

theseordersmoreheavilythanordersfurtheraway,partlybecausethoselatterorders

16

havetraditionallybeenmorelikelytobefake.(Analgorithmsummingtheoffersin

Figure1mightassignaweightof1.0totheoffersat$45.00;aweightof0.5tooffersat

$45.01;0.25tooffersat$45.02;etc.)However,afakeorderatthebestbidorofferprice

isalsodangeroustotheintendedspoofer,becauseitismuchmorelikelytobeexecuted

beforeitiscancelled(itwouldbeparticularlydangerousforaslowhumanbeingrather

thanafastalgorithmtoattempttospoofinthisfashion).

Whatfirstledmyinterviewee’sfirmtosuspectspoofingwasbehaviouratodds

withthenormalinteractionorderofqueuing.Itinvolveduseofthe‘modifyup’

instructionofGlobex(theelectronictradingsystemoftheChicagoMercantile

Exchange),whichaltersanexistingbidorofferbyincreasingthenumberoffutures

contractsbeingbidfororoffered.Ifthisinstructionisemployed,theorderthathasbeen

modifiedgoesthebackofthequeue(asinthecaseoftheofferof600sharesat$45.00in

Figure1).‘YoushouldneverdothatinaFIFOmarket’,saidmyinterviewee.(FIFOisthe

acronymof‘firstin,firstout’,andreferstotheformofqueuingdiscussedinthispaper,

inwhichthefirstorderatagivenpricereceivedbythematchingengineisexecuted

first.)2Doingsomethingthatcausedanordertolosequeueposition‘lookedweirdtous’,

theintervieweereported.Oneinterpretationmighthavebeenthatthiswas

‘incompetent’or‘maladjusted’(Livingston,1987:14)queuingbehaviour,butmy

interviewee’sfirmtookittobeevidenceofspoofing.Byusing‘modifyup’,ifnecessary

repeatedly,anordercouldbekeptatthebackofthequeue,whichisofcourseexactly

whatanalgorithmthatisspoofingneedstodotoreducetheriskoftheorderbeing

executed.

Fascinatingasspoofingis,itdoesnotexhaustthepossibilitiesofalgorithmic

dissimulation.Executionalgorithmsare,asnotedabove,usedbyinstitutionalinvestors

2Hisimplicitcontrastiswiththe‘prorata’marketsmentionedinAppendix1,inwhich‘modifyup’canbeemployedwithoutdetrimentaleffects.

17

tosplituplargeorders;alongwithhigh-frequencytrading,theyaretheothermost

importantformofalgorithmictrading.Theirentirerationaleisasaformof

dissimulation:thegoalisforaslongaspossibletohidethefactthatabig‘parent’order

(perhapsforamillionormoreshares)isbeingexecuted,bysplittingitinto‘child’

ordersforasfewas100shares.Asanintervieweewhoheadedamajorenterprise

providingexecutionalgorithmsputit:

we’lltakethathugeorderandchopitupintolittletinypieces,andifwedoit

rightanyonewho’slookingatitcan’ttellthatthereisabigbuyer:itlookslike

tinylittleretailishtrades[thesortoftradesalayinvestormightengagein]…My

jobistryingtoobscurewhatmyinstitutionalclientsaretryingtodo,youknow,

soourroleinthemarketplaceistomakeitsono-onecanworkoutwhatthe

hell’sgoingon.

Unlikespoofing,thisformofdissimulationisnotmerelylegalbutviewedasentirely

legitimate.Indeed,themostcommonformofmoralframingindebateoverhigh-

frequencytrading(see,e.g.,Lewis2014)istodistinguishthe‘good’algorithmsand

technicalsystemsthathidebigordersfromthe‘bad’HFTalgorithmsthatseektodetect

thebigparentorderandchangetheirpricingandordersubmissionbehaviour

appropriately.Thatframing,however,iscontingentandcontestible.Thus,one

interviewee,exasperatedwithwhathetooktobeitsfacilemoralism,reversedit:‘Idon’t

thinktheguywho’stryingtohidethesupply-demandimbalance[byemployingan

executionalgorithm],whyisheanybetterofahumanbeingthanthepersontryingto

discoverwhatthetruesupply-de[mandimbalanceis]?’

‘[T]hedependencyofinteractionalactivityonmattersoutsidetheinteraction’

ForGoffman,interactionshavetheirownlogicsandprocesses,andinteractionis‘a

particularkindofactivity’,whichiswhatwarrantsspeakingof‘theinteractionorder’

justasonemightreferto‘theeconomicorder’(Goffman,1983:5).Goffman,however,

18

alsorejectedwhathecalled‘arampantsituationalism’(1983:4).Heemphasised

repeatedlythat,inwordsalreadyquotedabove,whatgoesonininteractiondepends‘on

mattersoutsidetheinteraction’,includingsocialrelationshipsandsocialstructure.

Althoughhisdiscussionofhowsituationsandstructuresinterrelateisnotasfully

developedasonemightwish(seeBurns,1992),thebroadoutlinesofGoffman’saccount

areclear.Thereisonlya‘loose-coupling’relationship(Goffman,1983:12)between

situationsandsocialstructure,butthelatterisarealphenomenon,notreducibletoan

aggregateofmultipleinteractions.Socialrelationshipsandsocialstructureshape

interactions,butnotdeterministically:forexample,thetheoreticalinterestforGoffman

ofthequeueis(asindicatedabove)preciselythatitisaformofinteractioninwhich

theirinfluenceis,locally,blocked.

Letme,therefore,followGoffmanandgivethreeexamplesofthe‘loose-

coupling’shapingofalgorithmicinteractionby‘mattersoutside’it.Thefirstisthe

changingstatusofspoofing.WhenIbeganinterviewingin2010,spoofingseemeda

routinemarketpractice,atleastinfuturestrading:‘mostneworders[inthefutures

market]arefake’,atraderinChicagotoldmein2014.Therewasalongtraditionof

spoofingbeingacceptable–inChicago’stradingpits,Iwastoldbyanotherinterviewee,

asuccessfulspooferwasevenadmired,muchasaskilledblufferinpokerwouldbe–

andatolerantattitudecontinuedintheearlyyearsofthetransitiontoelectronictrading

(Zaloom,2006;Arnoldi,2015).Recently,however,disapprovalhasgrownsharply,even

thoughtwoofthemorelibertarian-mindedofmyintervieweesstillfeltstronglythatit

wasquitewrongforthestatetotrytotakeactionagainstspoofing.Until2014,traders

whohadengagedinspoofinghadonlyeverbeensubjecttoadministrativeaction,and

theresultantfinescouldineffectbeconsideredabusinessexpense.However,section

747oftheDodd-FrankAct(themainpost-crisislegislationintheUS)weakenedthe

legalteststhathavetobepassedforacriminalprosecutionforspoofingtosucceed,and

inOctober2014thefirstsuchprosecutionbegan.Thetraderwhotoldmeaboutthe

19

extentoffakeordersalsoreportedthatinthethreeweekssincetheindictment,the

incidenceofspoofing,asdetectedbyhisfirm’salgorithms,hadgonedownsharply.

ThesecondexampleconcernstheshapingofqueueinginUSsharetradingby

Federalregulation.AssummarisedinAppendix2,USstockexchangesarenotfreeto

havetheirmatchingenginesstructurequeuesastheywish.Instead,matching-engine

behaviourisgovernedbyRegulationNMS[NationalMarketSystem],which,although

firstimplementedonlyin2007,hasrootsthatcanbetracedbacktothelate1970s

(Pardo-Guerraforthcoming).Backthen,theSecuritiesandExchangeCommission–long

suspiciousofthedominanceofoneexchange,theNewYorkStockExchange(NYSE)–

sought,withamandatefromCongress,tocreateaNationalMarketSystemthatwould

promotecompetitionwithoutleadingtomarketfragmentation.Twodesignsforthat

systemcontended.One,backedbyprominenteconomists,wasforasingle,national

electronicorderbooktowhichallbrokersandexchangeswouldsendtheirorders.

Unsurprisingly,theNYSEandmostofthemoreminorexchangessawthisproposalasa

threattotheirexistence,andsuccessfullypromotedanalternativemodelinwhichthey

wouldcontinuetooperatemuchastheydid,butlinkedbyacomputernetworkthat

couldbebuiltquicklyandeasilyusingexistingNYSEtechnology.Fortyyearson,that

remainsthebasicstructureofUSsharetrading.Thedifferentexchangesarestillnot

fusedintoasingleorderbook.Instead,RegulationNMS’selaboraterulesarestillseenas

necessarytocompetition.

Itisdifficulttoreadthishistorywithoutthinkingoftheprescientanalysisof

neoliberalisminFoucault’slectureson‘TheBirthofBiopolitics’,delivered(asit

happens)in1979,justasthecrucialdecisionswerebeingtakenastohowtocreate

more‘competition’inUSsharetrading.Competitionisnotanaturalcondition,the

Ordoliberalsbelieved:rather,ithastobe‘producedbyanactivegovernmentality’

(Foucault,2008:121).Althoughtheinfluencesonithavebeenmorediffuse,the

20

SecuritiesandExchangeCommissionhasbeenthechiefvehicleofthatgovernmentality

inUSfinancialmarkets,andbyconstraininghowmatchingenginesorganisequeuesit

hassignificantlyshapedtheinteractionorderofalgorithms.

Mythirdexampleisadomainofautomatedtradinginwhichtherehasbeenno

analogueofthatprojectofgovernmentality:foreignexchange.(Financialregulationsare

stilllargelyprimarilynationalinscope,whileforeignexchangeisintrinsicallyan

internationalactivitythatthereforefallsintoagapinregulatorycoverage.)Inforeign

exchange,thetraditionallydominantactors–thebigglobalcommercialbanks–have

retained,atleastuntilveryrecently,adegreeofmarketpowerthatbankshavelargely

lostinotherexchange-basedtrading.However,weigheddownbyold‘legacy’software

systems,andfrequentlybureaucratic,bigbanksareoftennotgoodatthedevelopment

ofthefast,sophisticatedalgorithmsneededforHFT.Whenhigh-frequencytradingof

foreignexchangebegan,thealgorithmsdeployedbysmallHFTfirmsthereforefound

plentifulopportunitiesforprofitableaggressivetrading,oftenattheexpenseofbanks’

slowersystems.Banks,however,havebeenabletoexertinfluenceontradingvenues

thathashadtheeffectofshuttingoffmanyofthoseopportunitiesandthusrendering

liquidity-takingunprofitable.Theyhave,forexample,demanded(oftensuccessfully)

thattheirmarket-makingalgorithmsbegrantedprivileged‘lastlook’status:inother

words,matchingenginesgranttheiralgorithmsatimewindow–whichcanbeaslong

asasecond,whichisaneternityinHFT–inwhichtodecidewhethertopermitthe

matchingenginetoconsummateatrade.Lastlookandothermeasurestoconstrain

liquidity-takingbyHFTalgorithmshaveshiftedtheecologyofalgorithmsinforeign

exchange:intervieweesreportedawholesaleshiftfromliquidity-takingtoliquidity-

makingalgorithms.

Conclusion

21

Letmebeclearwhatthispaperisnotarguing.Itisnotclaimingthathumansand

algorithmsareidenticalbeings:plainlytheyarenot.Eveninthebriefnarratives

presentedabove,theirdifferentrolesareclear.Itishumanbeings,notalgorithms,that

areangeredbyperceivedqueuejumping.Itishumans,notalgorithms,thatare

prosecutedforspoofing,andthetraditionallegaltest–weakenedbytheDodd-Frank

Act,butlikelystilltobeprominentinthecomingtrials–ishumanintent:didMrSarao,

forexample,intendtomanipulatethemarket?

Nevertheless,theprevioussectionsofthispaperhave,Ihope,shownthatthe

limitedformsofactionavailabletotradingalgorithms(tosubmitorders,tocancelthem,

andsometimestomodifythem)cannonethelessgiverisetorichformsofstrategic

interaction.Algorithmsusewhatevermeansaremadeavailabletothemtogettothe

frontoftheelectronicqueue;theydissimulate(sometimeslegitimately,sometimesnot);

theyseektodefendtheirprocessesofinferenceagainsttheeffectsofdissimulation;

someenjoyprivilegedpowersdeniedtoothers.Thereisanincreasinglystrongly

policed,butstillvaguelydefined,boundarybetweenlegitimatestrategicactionand

illegalspoofing.Astheboundaryhardens,sothenatureofstrategicalgorithmicaction

shifts.3Itisindeedperfectlypossiblethatinthekindsofmarketsdiscussedhere,

algorithmsnowactmorestrategicallythanhumanscan.4Theveryfactthathuman

passionsareraisedbyalgorithmicqueuingandspoofing,andthatthelattercanleadto

jail,isindirectlytestimonytotherichnessofhowalgorithmsinteract:weseeinthat

interactionechoesofhowwehumansinteract.AsKnorrCetinacommentedinresponse

3Theintervieweewhotoldmeaboutthedeclinein‘classic’formsofspoofingfollowingthefirstcriminalindictmentalsosaidthattheywerebeingreplacedbyordersthatwerestillgoingtobecancelled,butacted‘epistemologically’(byrevealinghow‘real’otherordersintheorderbookwere)ratherthanbyimmediatelyprofitable(butdetectableandlegallyproblematic)trades.4Itis,forexample,harderforahumanspoofer(whocanonlyactslowly)tohidehisorhertraces,forexample,byusingmultiplesmallorderswithrandomsizes,ratherthanasingleall-to-obviousbigorder.

22

toaworkshoppresentationofthispaper,thenotionof‘theinteractionorderof

algorithms’hasacertainphenomenologicaladequacy.

Thebriefdiscussioninthesectionimmediatelybeforethisconclusionalso

demonstrates,Iwouldargue,therelevanceofoneofthemainreasonsGoffmangavefor

‘isolatingtheinteractionorder’:thatit‘providesameansandareasontoexamine

diversesocietiescomparatively,andourownhistorically’(Goffman,1983:2).Look

comparativelyacrossassetclasses(contrasting,forinstance,foreignexchangeand

sharetrading),orhistoricallyexaminehowtradinghaschanged,andyoufindin

algorithmicinteractionnotjustemergentphenomena,generatedreflexivelyandlocally,

butalsothetracesofwiderprocesses:theeffortstooutlawspoofingandthuskeep

orderbooks‘pure’;thecontinuingmarketpowerofbigbanksinforeignexchange;even

perhapsthedecades-longneoliberalprojecttogivecompetition–thatunnatural,

‘fragile’thing–a‘real,historicalexistence’(Foucault,2008:131-32).

Modesty,though,isalsorequired,forbynowthereaderwillsurelyhavenoticed

amethodologicalirony.Thispaperhasnotemployedthepreferredmethodologyof

interactionistsociology,participantobservation.Remarkably,giventhatHFTfirms

protecttheirintellectualpropertyfiercely(evengaininginterviewaccessisinmany

casesimpossible),RobertSeyfertoftheUniversityofKonstanzand,especially,Ann-

ChristinaLangeoftheCopenhagenBusinessSchoolhavegainedadegreeof

observationalaccesstoHFTfirms(seeBorch,HansenandLange,2015).Observingan

HFTfirm,however,isnotthesameasobservingalgorithms.Algorithmswere

interactinginCermakwhenIvisitedthatdatacentre,butwereofcourseinvisibletome.

Tobedependent,inconsequence,onthetestimony(oreventoobservetheactions)of

thehumanbeingswhowriteandusetradingalgorithmsistorelyuponindirect

evidencethatcanmislead.AsoneofmyHFTintervieweeswarnedme:‘someonecould

23

beinallhonestysayingthey’re[theiralgorithmsare]doing[something]wheninfact

they’redoingsomethingelse,they’rejustnotmeasuringitright’.

Theinteractionofalgorithmsdoesleaveitstracesinchangesinorderbooksand

inprices.However,intheorder-bookandpricedataavailabletoacademicresearchers,

trading-accountidentifiersarealmostalwaysremoved,makingitdifficultorimpossible

toidentifysequencesofactionsbythesamealgorithmoreventhesametradingfirm.

Researchersemployedbyregulatorybodiesdohaveaccesstoaccountidentifiers,but

theyhavefoundthetaskofunravellingpatternsofalgorithmicinteraction(evenin

shorttimeperiods)computationally,andperhapsconceptually,closetointractable.Six

yearson,thereisstilldebateonthecausesofthe‘flashcrash’,atwenty-minutespasmin

theUSfuturesandstockmarketson6May2010.Aworkingpartyfromfiveregulatory

bodiesspentmonthsseekingtodisentangleabroadlysimilareventintheUSTreasury

bondmarketbetween9:33a.m.and9:45a.m.on15October2014,butconfessed

themselvesunablefullytoidentify‘[t]hedynamicsthatdrove…trading’inthosetwelve

minutes(JointStaffReport,2015:33).Furthermore,anyGoffmanianwantstosee

analysesofroutine,notjustunusual,interaction,butresearchersemployedbymarket

regulatorsunderstandablyoftenneedtofocusontheunusual.

Weare,inshort,stillfarfromhavingarobustunderstandingofhowtrading

algorithmsinteract.However,thevirtueoftheconceptof‘interactionorder’isthatit

focusesourattentionontherightissue,whichisindeedinteraction.Anyindividual

tradingalgorithmcanperfectlyreasonablybeseenasthe‘delegate’ofahumanbeingor

beings(althoughmyinterviewee’swarningoftheirpossiblydefectiveunderstandingof

itsoperationsmustbeborneinmind).Buttheensembleofinteractingalgorithmsisnot

ourindividualorcollectivedelegate,andwhiletheprogramtextofatradingalgorithm

mayusuallyremainunchangedbyinteraction,howitmateriallyactsisshapedby

interaction.Evenindividualalgorithmsthusneedtobeunderstoodrelationally,inthe

24

spiritofGoffman’sunfortunatelywordedbutsuccinctsummaryofhisrelational

sociology:‘Not,then,menandtheirmoments.Rathermomentsandtheirmen’

(Goffman,1967:3).

Appendix1:EmpiricalNuances

Therearetwoothermainways(beyondtheentryandcancellationoforders)inwhich

tradingalgorithmsinteract.Thefirstisinelectronictradingvenuesthat,unlikethose

discussedinthetext,donothavecentralorderbooks.Forexample,somevenues

(especiallyinbondtradingandforeignexchange)haveinsteadafixeddistinction

betweenparticipants,generallyalgorithmic,thatareallowedtopostbidsandoffers–

eitherinresponsetorequeststodoso,orintheformofconstantly‘streamed’prices–

andotherparticipants,generallyhumanbeings,thatcannotpostpricesbutcanonly

acceptpricespostedbyothers.Second,evenalthoughthedifferentalgorithmsbeingrun

byatradingfirmdonot(asfarasIcantell)usuallycollaborate,theycanhaveeffectson

eachother.Firmsnormallyhaveaggregaterisklimitsthatmean,e.g.,thatifone

algorithmhasbuiltupalargepositioninaparticularstock,othersarepreventedfrom

addingtoit.Alsocommonissoftwaretopreventself-trading(analgorithmselling

financialinstrumentstoanotherofthefirm’salgorithms),whichincursunnecessary

expensesandmayattractunwelcomeregulatoryattentionaspotentiallysettinga‘false

price’.Thissoftwarehastheeffect,e.g.,thatifonealgorithmisofferingsharesatagiven

price,then(dependentonthesoftware’ssettings)eitherallofthefirm’sother

algorithmsarepreventedfrombiddingtobuysharesatthatpriceortheoriginalofferis

cancelled(ineffectbyanalgorithmotherthantheonethatsubmittedit).

Localinteractionsofthiskindamongalgorithmsareoftheoreticalinterest,for

exampleifonewishestoapplymetaphorssuchas‘swarming’.Alsointerestinginthis

respectisthatalgorithmscansometimeslearn‘locally’aboutorder-bookchangesviaa

25

‘confirm’–anelectronicmessagereportingexecutionofoneofafirm’sorders–before

thecorrespondingmessageappearsintheoveralldatafeed.

Thediscussionofordersinthetextisalsonotexhaustive.Aswellastheorders

described(whichmarketparticipantswouldcall‘limitorders’,i.e.orderstobuyator

belowaspecifiedprice,ororderstosellatoraboveit),itis,forinstance,alsooften

possibleforanalgorithmorhumantradertosubmita‘marketorder’.Thisisanorder

simplytobuyortosellatthebestavailableprice,anditcanthereforeunderalmostall

circumstancesbeexecutedimmediately.Theorderbookthuscontainsonlylimitorders,

notmarketorders,whichiswhythefullernameforitis‘centrallimit-orderbook’.

Whilemostmatchingenginesoperateatime-prioritysystemofthekind

describedinthetext,aminorityemploy‘pro-rata’matching,inwhichnewexecutable

ordersarematchedagainstexistingordersinproportiontothesizeofthelatter.Certain

‘designatedmarketmakers’(forexampleinoptions)mayalsobeguaranteedaspecific

proportionofanyincomingexecutableorder.

Appendix2:IntermarketSweepOrdersandSpecialOrderTypes

RegulationNMSineffectdecreesthatbeforeanexchange’smatchingengineaddsa

displayableordertoitsorderbookitmustcheckthebest(i.e.highestpriced)bidand

best(i.e.lowestpriced)offeravailableatallotherUSexchanges.Amatchingengine

cannot,forexample,addtoitsorderbookanoffertosellatthepriceofthenationalbest

bid,butmustelectronicallyroutethatorderforexecutiontotheexchangewhosebook

containsthatbid.Inconsequence,incomingordersforsharesareoftendelayedwhile

thematchingengineperformsthischeckandwaitsforittobepermissibletoaddthem

totheorderbook.

26

RegulationNMSthusdirectlyshapesqueuing,andhasgivenrisetoavarietyof

waysinwhichalgorithmscanimprovetheirqueuepositions.Themostimportantand

mostprevalentisanexceptioninRegulationNMS(SecuritiesandExchange

Commission,2005:37523)thatprovidesforan‘IntermarketSweepOrder’.Thisisan

orderbearingacomputerisedflagindicatingthatotherordershavebeensenttoother

exchangesthatwillexecuteagainst,andthusremovefromtheirorderbooks,anyorders

thatblocktheadditionoftheflaggedordertotheorderbook.However,onlyregistered

broker-dealersandcustomersauthorisedbythemareallowedtousetheIntermarket

SweepOrderflag.

Exchangeshavethemselvesalsodesignednewtypesofspecialisedorders,

whichoftenhingeonthefactthatRegulationNMSgovernstheentryofdisplayable

orders,nothiddenorders.Matchingenginesalwaysallocatehiddenorderspositionsin

theorder-bookqueuebehinddisplayableordersatthesameprice,butifthose

displayableorderscannotbeaddedtotheorderbookbecauseoftheconstraintsof

RegulationNMS,aninitiallyhiddenordercanstillgettotheheadofthequeue.Best

knownofthesenewordersisonemadeavailablebytheexchangeDirectEdgecalled

‘HideNotSlide’(Anon,2009).

Acknowledgement

TheresearchreportedherewassupportedbytheEuropeanResearchCouncil(grant

291733).IamhugelygratefultoTCS’sthreerefereesforhelpfulfeedback.

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