从深度学习到大语言模型：量化投资中的人工智能技术综述

FromDeepLearningtoLLMs:AsurveyofAIinQuantitativeInvestment

arXiv:2503.21422v1[q-fin.CP]27Mar2025

BOKAICAO,TheHongKongUniversityofScienceandTechnology(Guangzhou),ChinaandIDEAResearch,InternationalDigitalEconomyAcademy,China

SAIZHUOWANG,TheHongKongUniversityofScienceandTechnology,HongKongandIDEAResearch,InternationalDigitalEconomyAcademy,China

XINYILIN,XIAOJUNWU,andHAOHANZHANG,TheHongKongUniversityofScienceandTechnology(Guangzhou),ChinaandIDEAResearch,InternationalDigitalEconomyAcademy,China

LIONELM.NI,TheHongKongUniversityofScienceandTechnology(Guangzhou),China

JIANGUO∗,IDEAResearch,InternationalDigitalEconomyAcademy,China

Quantitativeinvestment(quant)isanemerging,technology-drivenapproachinassetmanagement,increasinglyshapedbyadvancementsinartificialintelligence.Recentadvancesindeeplearningandlargelanguagemodels(LLMs)forquantfinancehaveimprovedpredictivemodelingandenabledagent-basedautomation,suggestingapotentialparadigmshiftinthisfield.Inthissurvey,takingalphastrategyasarepresentativeexample,weexplorehowAIcontributestothequantitativeinvestmentpipeline.Wefirstexaminetheearlystageofquantresearch,centeredonhuman-craftedfeaturesandtraditionalstatisticalmodelswithanestablishedalphapipeline.Wethendiscusstheriseofdeeplearning,whichenabledscalablemodelingacrosstheentirepipelinefromdataprocessingtoorderexecution.Buildingonthis,wehighlighttheemergingroleofLLMsinextendingAIbeyondprediction,empoweringautonomousagentstoprocessunstructureddata,generatealphas,andsupportself-iterativeworkflows.

CCSConcepts:•Generalandreference→Surveysandoverviews;•Appliedcomputing→Economics;

•Computingmethodologies→Artificialintelligence;Machinelearning.

ACMReferenceFormat:

BokaiCao,SaizhuoWang,XinyiLin,XiaojunWu,HaohanZhang,LionelM.Ni,andJianGuo.2018.FromDeepLearningtoLLMs:AsurveyofAIinQuantitativeInvestment.ACM/IMSJ.DataSci.37,4,Article111(August2018),

37

pages.

/XXXXXXX.XXXXXXX

1INTRODUCTION

Assetmanagementisacrucialandexpandingsegmentofthefinancialindustry,withQuantitativeInvestment(Quant)emergingasakeyapproachwithinit.Quantitativeinvestmentstrategies

*CorrespondingAuthor.

Authors’addresses:BokaiCao,mabkcao@,TheHongKongUniversityofScienceandTechnology(Guangzhou),ChinaandIDEAResearch,InternationalDigitalEconomyAcademy,China;SaizhuoWang,swangeh@connect.ust.hk,TheHongKongUniversityofScienceandTechnology,HongKongandIDEAResearch,InternationalDigitalEconomyAcademy,China;XinyiLin,xlin652@;XiaojunWu,xwu647@;HaohanZhang,hzhang760@,TheHongKongUniversityofScienceandTechnology(Guangzhou),ChinaandIDEAResearch,InternationalDigitalEconomyAcademy,China;LionelM.Ni,TheHongKongUniversityofScienceandTechnology(Guangzhou),China,ni@ust.hk;JianGuo,IDEAResearch,InternationalDigitalEconomyAcademy,China,guojian@.

Permissiontomakedigitalorhardcopiesofallorpartofthisworkforpersonalorclassroomuseisgrantedwithoutfeeprovidedthatcopiesarenotmadeordistributedforprofitorcommercialadvantageandthatcopiesbearthisnoticeandthefullcitationonthefirstpage.Copyrightsforcomponentsofthisworkownedbyothersthantheauthor(s)mustbehonored.Abstractingwithcreditispermitted.Tocopyotherwise,orrepublish,topostonserversortoredistributetolists,requirespriorspecificpermissionand/orafee.Requestpermissionsfrompermissions@.

©2018Copyrightheldbytheowner/author(s).PublicationrightslicensedtoACM.

2831-3194/2018/8-ART111$15.00

/XXXXXXX.XXXXXXX

ACM/IMSJ.DataSci.,Vol.37,No.4,Article111.Publicationdate:August2018.

111

ACM/IMSJ.DataSci.,Vol.37,No.4,Article111.Publicationdate:August2018.

111:2BokaiCaoetal.

leveragestatisticalanalysis,optimizationtechniques,andincreasingly,AIalgorithmstoidentifyandexploitmarketinefficiencies.Benefitingfromtheexponentialgrowthindataavailability,computa-tionalpower,andtechnologicalinnovations,theseapproachessignificantlyimproveinvestmentdecision-makingandprovideacompetitiveedgeinthefinancialmarket.

Amongvariousquantitativeinvestmentapproaches,alphastrategyhasreceivedconsiderableattentionforitsstrongcapacitytocapturemarketinefficienciesanditsnaturalalignmentwithAI-drivenpredictivemethods.Thepursuitof’alpha’referstopredictingindividualasset’sexcessreturnsoverthemarket’soverallperformance,suchasastockindex,andisthecentralfocusofportfoliomanagers.Thedevelopmentofalphastrategiestypicallyincludesfoursteps:datapro-cessing,modelprediction,portfoliooptimization,andorderexecution(asintroducedinsubsection

2.2

).Thesefoursub-tasks,thoughdistinct,arecloselyinterconnected,allworkingtowardsthecommongoalofmaximizingexcessreturnswhilecontrollingrisks.Comparedtootherquantitativeinvestmentstrategies,suchashigh-frequencytradingorarbitrage,alphastrategieshavebeenshowntohavegreatcapacityandeffectivenessbyexploitingmarketmispricings.Asaresult,alphastrategiesreceivethehighestattention,researchfocus,andmarketshareofresearchersandinvestors,representingthecoretechnologyinquantitativeinvestment.Inthissurvey,wetakethealphastrategyasarepresentativeexampleofquantitativeinvestmentandcenterourdiscussiononhowAIplaysaroleinthisfield.

Inrecentyears,theapplicationofdeeplearning(DL)techniquesinalphastrategieshasshownpromisingresults,demonstratingtheabilitytoidentifycomplexpatternsandrelationshipsinfinancialdatathataredifficulttodetectusingtraditionalquantitativemethods.Meanwhile,largelanguagemodels(LLMs),suchasGPT-series[

4

],BERT[

38

],andtheirfinancialvariants,haveshownremarkablepowerinunderstandingcontextualdata,generatingaccurateinterpretations,andreasoninglikehumananalysts.Therefore,theirapplicationinfinance,particularlyinquantitativeinvestment,hassparkedendlesspossibilities.

Thispaperfocusesontheevolution,application,andrespectiveadvantagesofDLandLLMsinquantitativeinvestment,withaspecificemphasisonalphastrategies,providingacomprehensivereviewoftheexistingliterature,anddiscussingthepotential,challenges,andlimitationsofhowLLMscouldenhanceDL-basedapproaches.

1.1EvolutionofAlphaStrategyInvestment

Theevolutionofthealphastrategycouldbeencharacterizedbyathree-stageprogressionfrommanuallabelingoftradingsignalstotheuseofdeeplearningmodelsandultimatelytoaneraofagentinteractionanddecision-makingbetweenLLMagents(Figure

1

).Intheearlystages,thefocuswasontraditionalstatisticalmodelingofmarketpatterns,relyingontheexpertiseofindividualresearcherstoidentifyprofitabletradingsignalsanddevelopcorrespondingmodels.However,thisapproachhadlimitationsduetothecomplexityoffinancialmarketsandthedifficultyofcapturingallrelevantfactorsinamodel.Itstillreliesheavilyontheskillandexperienceofhumanresearcherstoevaluateandexecutetradingstrategies.

Asthefieldhasmatured,theapplicationofdeeplearninghasopenedupnewpossibilitiesforquantitativeinvestment.Particularlyinthecontextofalpharesearch,deeplearninghasbeenshowntobeeffectiveinidentifyinginherentpatterns.Forexample,deeplearningmodelshavebeenusedtoanalyzefactorssuchasspatialinterconnectedness[

142

],long-termtemporaldependence[

178

],andnewssentiment[

68

]topredictthepricemovementsandmanagepositions.Whiletheuseofdeeplearningholdsgreatpromise,therearealsochallengesthatneedtobeaddressed.Onemajorchallengeistheriskofoverfitting,whichcanleadtopoorperformancewhenthemodelisappliedtonewdata.Anotherchallengeliesinimprovinginterpretabilityandaccuracytofurtherunderstand,reason,andinteractwithvastvolumesofmultimodaldata.Despitethesechallenges,

FromDeepLearningtoLLMs:AsurveyofAIinQuantitativeInvestment111:3

Fig.1.TheevolutionaryprocessofAlphainvestmentacrossdifferentstages.

theapplicationofdeeplearninginquantitativeinvestmentisexpectedtocontinuetogrowinbothacademiaandindustry,asinvestorsseektogainanedgeinacompetitivemarket.

Morerecently,LLMshaveemergedasapowerfultoolinquantitativeinvestment,characterizedbyrapiddevelopmentandenormousapplicationpotential,attractingsignificantattention.Theyexcelinunderstandingandprocessingmultimodaldataandpossessthepotentialtoautonomouslyhandlecomplextasksofreception,comprehension,andinferenceoverlarge-scaledatasets.Incurrentmainstreamresearch,LLMsprimarilyservetworolesinalphastrategy:aspredictors(subsection

4.1

)andasagents(subsection

4.2

),handlingvarioustasks.Inbothcases,theycontributehigh-levelinsightsbuiltupondeeplearningframeworksandhavethepotentialtofurtherevolveAI-powereddeeplearninginvestmentmethodsintotheAI-automatedstage.However,thepracticaldeploymentofLLMsisstillinitsearlystages.Wewillhighlighttheircurrentlimitationsanddiscusspotentialfuturedirectionsfortheirdevelopment(subsection

4.3

).

1.2MotivationandContributionofthisSurvey

TheuseofdeeplearningandLLMsinalphastrategiesofquantitativeinvestmenthasrecentlyseenasurgeofinterest,withmanystudiesfocusingontheapplicationinvariousaspectsofthealpharesearchpipeline.However,mostofthesestudiesarerelativelyisolatedinspecifictasksordisciplines,andthereisalackofaunifiedviewofthewholelandscapeofquantitativeinvestment,particularlyinthecontextofalphastrategy.Thissurveywillalsosystematicallysummarizetheevolutionofalpharesearchfromtheperspectiveofphasedalgorithmicevolution.Additionally,quantisafieldwhereresearchandpracticearehighlyinterconnected,whileexistingsurveypapersarelimitedtofillingtechnicalgapsbetweenpracticalopportunitiesandresearchtheoriesrelatedtothecombinationofLLMsandDL-basedalphamodels.There’salackofacomprehensiveframeworkandforward-lookingperspectiveonthefutureofdeeplearningandLLMsresearchworkflowsfromareal-worldviewpoint.

Toaddresstheseissues,thissurveypaperaimstoprovidereaderswithamoreintegratedandcomprehensiveviewofthealphastrategy.Weintendtoachievethisbysurveyingrelevantworks

ACM/IMSJ.DataSci.,Vol.37,No.4,Article111.Publicationdate:August2018

.

111:4BokaiCaoetal.

Fig.2.Theoverallframeworkofthispaper.

37,No.4,Article111.Publicationdate:August2018.

ACM/IMSJ.DataSci.,Vol.

ACM/IMSJ.DataSci.,Vol.37,No.4,Article111.Publicationdate:August2018.

FromDeepLearningtoLLMs:AsurveyofAIinQuantitativeInvestment111:5

thatcoveralltypesofdeeplearningandLLMstasksinthewholealphapipeline,offeringaholisticandinterconnectedviewofthefield.Moreover,oursurveypaperseekstoprovideabroaderresearchperspectivebystartingfromreal-worldapplications,highlightingthepracticalissuesandchallengesfacedbyinvestorstorevealthegenericresearchproblemsandpromotefuturestudie.TheoverallframeworkofthispaperisshownasFigure

2

.

Keycontributionsofthispaperinclude:

•Itprovidesacomprehensivesurveyoftheexistingresearchontheuseofdeeplearningandlargelanguagemodelsinalphastrategies,connectingdifferentworksinaconcreteresearchpipeline.Thissurveyisthefirstofitskindtocoverthetopiccomprehensively,offeringaholisticviewofthefield.

•ItIntroducesthedomainfromaninterdisciplinarystandpoint,emphasizingpracticalap-plicationstoderivekeyresearchquestionsforquantitativeinvestigations.Additionally,itdiscussesthemostchallengingproblemsfromapracticalperspectiveandoffersinsightsintopotentialfutureresearchdirections.

•Itsystematicallycomparesthetechnicalapproaches,strengths,andweaknessesacrossthethreestagesofquantitativeinvestment:traditionalstatisticalmodels,DL-basedmethods,andLLM-basedapproaches.Buildingontheiterativedevelopment,itidentifieskeygapsandleadsalphastrategiestothenextstage.

2BACKGROUND

Inthissection,weprovideabriefoverviewofalphastrategyanditsroleinquantitativeinvestment.Specifically,webeginbyintroducingalphastrategiesandthendiscussthealphapipelineinquant,whichservesastheframeworkfordevelopingandimplementingtheinvestmentprocess.

2.1AlphaStrategy

Alphastrategiesareinvestmentapproachesthatfocusonidentifyingopportunitiesyieldingreturnsthatexceedthemarketbenchmarkbyexploitinginefficienciesormispricings.Thesestrategiestypicallyconsistoftwocomponents:thealphasideandthehedgingside.

Thealphasideofanalphastrategyfocusesongeneratingexcessreturnsbyidentifyingprofitableinvestmentopportunities.Toachievethis,itaimstomakeaccuratepredictionsaboutthetrendsofindividualinstruments,sectors,ortheoverallmarket.Thesepredictionsarethenusedtoallocatecapitaltodifferentinvestmentinstrumentstomaximizereturns.Thealphasidefacesseveralcommonchallenges.Forexample,predictingassetpricesinvolveshandlinghigh-dimensional,noisydatawithcomplexpatterns,whileportfolioallocationrequiresoptimizingthetrade-offsbetweenriskandreturn.Deeplearningtechniqueshavebeenshowntobeeffectiveinaddressingthesechallenges,whichiswhyrecentworkshaveapplieddeeplearningtoalphastrategies.

Thehedgingsideofanalphastrategyfocusesonmanagingtherisksassociatedwithmarketmovements.Itsgoalistomitigatemarketrisks,ensuringthattheexcessreturnsgeneratedbythealphasidearenoteroded.Toachievethis,thehedgingsidetypicallyemployshedgingportfolios,suchasderivativeslikestockindexfuturesoroptions,whileaimingtominimizehedgingcosts.Thehedgingsidealsofacesseveralcommonchallenges.Forexample,selectingtheappropriatehedgingportfoliorequiresunderstandingtherelationshipbetweentheportfolioandtheunderlyinginvestments.Additionally,hedgingportfoliosmayincurtransactioncosts,whichcanaffectreturns.

Whileboththealphasideandthehedgingsideofanalphastrategyaimtogeneratereturnswhilecontrollingrisks,theyfacedifferentchallengesandrequiredistinctsolutions.Inthispaper,weprimarilyfocusonthealphasideofalphastrategiesanddiscussrecentworksthathaveemployeddeeplearningtechniquesandlargelanguagemodelstoaddressthechallengesfacedbythealpha

111:6BokaiCaoetal.

Adjustments

Monitor

InvestmentResearchExecution

RiskanalysisDecomposition

Pre-processingFactorminingModellingPortfoliooptimizationOrderexecution

ImputationCombinationLearningReturnObjectiveScheduling

Measurement

Trading

StandardizationSelectionReasoningOptimizationNetwork

CleaningExtractionInferenceRiskConstraintsAlgorithmic

Management

Programming

Returns

DataFactorsAttributesPositionsOrders

QuantitativeInvestmentSystem

RawData

Feedback

Exchange

Fig.3.Atypicalpipelineofquantitativeinvestment.

side.However,itisimportanttonotethatthehedgingsideisalsoanessentialcomponentofalphastrategies,andinterestedreaderscanrefertorelevantliteratureforfurtherstudy.

2.2AlphaPipeline

Initsessence,analphastrategyaimstoidentifywhichinstrumentstotrade,andactuallytradethemtoearnprofits.Initsmostsimpleform,thisideacanbepracticedinaone-stepway.Theinvestorcanjustlookatthemarket,findsomeassetshewantstotrade,andputthecorrespondingordersonthemarket.

However,thingsbecomemuchmorecomplexwhenthecapacityofthestrategygrowsintermsofboththeamountofmoneyandthenumberofassetsinvolvedintheinvestment.Inthiscase,tradingdecisionscannotbecarriedoutjustwithsimplehumanoperations.Instead,thewholestrategyneedstobestandardizedintoseveralsub-tasks,whereeachtaskiswell-formulatedandhasawell-developedtoolboxtodealwithit.AsshowninFigure

3

,analphastrategycanbedecomposedintoapipelineconsistingofseveralsub-tasks,whichwewillillustrateinthefollowing.

•Dataprocessing:Thewholepipelinestartsfromanalyzingthedatafromthemarket,andthedatacomeinvariousforms.Toperformmoderndataminingtechniquesonthesedata,thedatamustbefirstcleanedandstandardizedintounifiedforms.Thepre-processingstepishenceinvolvedtodothecleaning,standardization,andimputationofdata.Meanwhile,pre-processeddatacandevelopedintofeaturestobetterintegratemarketinformationandserveasinputforthenextstageofthemodel.

•Modelprediction:Althoughinvestmentscanbedonewithvariousmotivations,themostimportantoneshouldbetheexpectedfutureperformanceoftheassetprice.Therefore,toformaninvestmentdecision,weneedtofirstpredictthefutureoftheassetsofinterest,andthenmakedecisionsaccordingly.Pricepredictionaimstomakepredictionsaboutassets,suchastheirfuturepricechange,volatility,etc.Thispredictiontaskiswhatdeeplearningisgoodat,soabunchofdeeplearningtechniqueshavebeenstudiedinmakingbetterandmoreaccuratepredictions.

•Portfoliooptimization:Modelpredictionsthemselvescannotbedirectlyusedasinvestmentdecisions.Theyareutilizedintheportfoliooptimizationstagetogenerateinvestmentdecisions.Essentially,theportfoliooptimizationsteptakesthevariouskindsofmarketpredictions(e.g.

37,No.4,Article111.Publicationdate:August2018.

ACM/IMSJ.DataSci.,Vol.

FromDeepLearningtoLLMs:AsurveyofAIinQuantitativeInvestment111:7

ACM/IMSJ.DataSci.,Vol.37,No.4,Article111.Publicationdate:August2018.

Table1.Categorizationoffinancialdata.

Modality

Features

Example

References

Numerical

Quotedata

Regularinterval

Quotesthataregeneratedatregulartimeintervals

1-minutecandlestickchart

[131,

149]

Irregularinterval

Quotesthataregeneratedatirregulartimeintervals

Tick-leve

l1

orderbookdata

[16]

Fundamentaldata

Fundamentaldatasuchasrevenuesandprofits.

Financialstatements

[134]

Relational

Pairwiseedges

Therelationbetweenapairofentities

Knowledgegraph

[46,

75,

159,

160]

Hyperedges

Therelationinvolvingasetofentities

Sectorcategorization

[127]

Alternative

Text

Informationexpressedinnaturallanguage.

Socialmediaposts

[59,

87,

128,

162]

Images

Imagesthatarerelatedtothetradedasset

Satelliteimages

[101,

118]

Othermodalities

Anything

WiFitraffics,cellphonesignals

[64]

Simulation

Timeseries

Syntheticquotedata

Simulatedorders

[50,

84,

183,

184]

Tabular

Databasestructuredinatabularform

Simulatedfinancialstatements

[47,

126,

133]

pricechange,volatility)asinput,andoutputsthecorrespondinginvestmentdecisionssuchastheamountofmoneyallocatedforeachassetinthenextholdingperiod(alsoknownaspositions).Thecoreprobleminthisstageistofindanoptimalportfoliooptimizationthatmaximizestheobjectivedefinedbyinvestorssuchasmaximizingtheexpectedrisk-adjustedreturns,subjecttotheconstraintsdefinedsimultaneously,suchasthemaximumvolatilityconstraintorthediversityconstraintdefinedforriskcontrol.Traditionally,thisproblemisformulatedasanoptimizationproblemandthecorrespondingoptimizationtechniquescanbeappliedtosolvethisproblem.

•Orderexecution:Theportfolioallocationsneedtobeimplementedbyactuallyputtingordersonthemarketandmakingthedeal.Andthisorderexecutionstepisbynomeansaneasytaskforalphastrategiesthatinvolvealargeamountofmoney.Thisisbecauseordersplacedonthemarketwillinevitablybringfluctuationsonthemarket,drivingthepricetotheoppositedirectionthatisbeneficialfortheinvestor.Andwhenthetradevolumebecomeslarge,thiseffectisalsomagnifiedandmightintroducegreatlosstothestrategy.Hence,theorderexecutionstageisinvolvedtominimizesuchlossbysplittingbigordersintosmalleronesanddecidingtheappropriatetimetoexecutethem.Traditionally,thisproblemhasbeenformulatedasanoptimalcontrolproblemwherethedynamicsofthelimitorderbookareextensivelystudied.

Theinvestmentpipelineisnotanopen-loopsystem.Instead,itneedsfeedbackfromthemarkettoadjustitsbehaviors.Themonitormoduleisthereforeinvolvedintheentirepipelinetoconductriskanalysis,collectfeedbacksignals,andmakecorrespondingadjustmentstothepreviousprocesses.Forexample,theriskmanagementmodulecalculatestheriskexposureofthecurrentportfoliotodifferentsectors.Then,fromtheperspectiveoftheoverallprocess,itmakesbalancingorneutralizationadjustmentsbyinjectingconstraintsoradjustingtheobjectivefunctionsintheportfoliooptimizationmodule.

3DEEPLEARNINGINALPHAPIPELINE

Deeplearninghasbeenwidelyappliedthroughoutthewholealphapipeline.Inthissection,wesystematicallyanalyzehowdeeplearningisutilizedtoenhancetraditionalalpharesearchateachsub-taskofthepreviouslydiscussedpipeline.

111:8BokaiCaoetal.

High

Uppershadow

Amount(BillionDollar)

Open

Real

Body

Close

Lowershadow

Low

(a)Candlestickchart[

1

](b)LimitOrderBook[

153

]

60

50

40

30

Q3/FY2021Q2/FY2021

Q1/FY2021

20

10

0

TotalRevenueGrossProfitNetincome

(c)FundamentaldataofMicrosoft.

Fig.4.Examplesofnumericaldata.

3.1DataProcessing

Asstatedbefore,thedatausedforalpharesearchneedtobefirstpre-processedintounifiedformat,andthenusedforprediction.Herewedividedatabeforeandafterpre-processingintotwotypes,namelyrawdata,andfeatures.

3.1.1RawData.Financialmarketsconstantlygenerateheterogeneousdatawithvariousmodalities.Basedontheirsourceandmodality,wecategorizefinancialdatausedinquantstrategiesasnumericaldata,relationaldata,alternativedataandsimulationdata.AsummaryandacomparisonofdifferenttypesofdataarepresentedinTable

1

andwewillelaborateoneachoftheminthefollowing.

Numericaldata.Numericaldataarethemostwidespreadtypeofdatainthefinancialworld,itcanbecategorizedasquotedatathatindicatethemovementofassetpricesofarbitraryfrequency,andfundamentaldatathatreflecttheoperationsoftheunderlyingeconomicentitiesofsecurities.

Quotedatatypicallyincludescandlestickchartandlimitorderbook,asillustratedinFigure

4

.Candlestickchartisawaytoillustratethepricemovementofafinancialinstrument,whichconsistspricesatfourdifferentinformationdimensioninantimeinterval,namelytheopen,close,high,andlowprices.Alimitorderbookreferstoanelectroniclistofbuyandselllimitordersorganizedbypricelevels.Fundamentaldataiswidelyusedbyanalyststodeterminetheintrinsicvalueofafinancialinstrument.Importantsourcesoffundamentaldataarebalancesheets,incomestatements,andcashflowstatementsfromfinancialstatements.

Financialmarketsareintrinsicallydynamic,numericaldataareoftentreatedastimeseries.

Undersuchcircumstances,averyimportantpropertyistheirfrequency,i.e.,theintervalatwhichnewdatapointsaregenerated.Generallyspeaking,fundamentaldatahavethelowestfrequency,changingeveryfewmonths.Incontrast,quotedatahavefrequenciesatmultiplelevels,rangingfromtheloweronessuchasweek-orday-leveltothehigheronessuchasminute-orsecond-levelfrequencies.Moreextremely,thequotedatawiththemaximumtemporalresolution,namelytick-leveldata,recordsthehistoryofeachorderandtransactionsequentially.

Relationaldata.Whilenumericaldatadescribesindividualfinancialentities,therelationshipsbetweenthemcanalsoimpactmarkettrends.Werefertosuchdataasrelationaldata,whichdescribestheubiquitousrelationshipsbetweentwoormorefinancialentities.Formally,relationaldataareusuallyrepresentedasagraphG=v×E,wherevisthesetofnodesandEisthesetofedges.Theedgescanbeeitherpairwiseedgesorhyperedges[

147

],basedonthenumberofentitiesinvolvedintherelations.Inpractice,relationaldatahaverichsemantics,rangingfromthebusinessrelationshipsbetweencompanies,tovariouseventsinvolvingfinancialentities,tothecorrelations

37,No.4,Article111.Publicationdate:August2018.

ACM/IMSJ.DataSci.,Vol.

ACM/IMSJ.DataSci.,Vol.37,No.4,Article111.Publicationdate:August2018.

FromDeepLearningtoLLMs:AsurveyofAIinQuantitativeInvestment111:9

(a)Pairwiseedges[

46

](b)Hyperedges[

127

]

Fig.5.Relationaldataexamples.

andcausalrelationshipsbetweenentitiesfromastatisticalperspective.AnillustrationofpairwiseedgesandhyperedgesispresentinFigure

5

.

(1)Pairwi