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Abstract
Stockselectionreferstoselectingoutstandingstocksbasedonagivensetofhistorical
stockpricesequences,inordertoachievebetteroverallperformanceininvestmenttrading(high
returnandlowrisk).Choosingstockswithhighinvestmentpotentialalsoplaysacrucialrolein
thedailyinvestmentportfolioandriskmanagementoffunds.Inrelevantfields,someexisting
researchbasedondeeplearninghasachievedgratifyingresults,whichhasattractedmoreand
moreresearchinterest.However,duetothehighstochasticityandcontinuousdynamicevolution
offinancialmarkets,thistaskisstillfullofchallenges.
Tocapturespatiotemporalfeaturesinstocksequences,andintegraterelevantmultimodal
auxiliaryinformation,thisarticleproposes:(1)Astockselectionmethodbasedonmultimodal
multi-scalegraphconvolutionalnetwork.Thismethodtakesthepredictionofstockpricefluc-
tuationsasthelearningobjectiveofthemodel,andselectsthetopkstockswithahigherproba-
bilityofriseforinvestmentbypredictingthefutureriseandfallprobabilitiesofeachstock.This
modelintroducesmediatextinformationandeffectivelyminesandalignsstockpriceandtext
datafeaturesthroughmultimodalfusinganddiffusingblocks.Themulti-scalearchitectureof
thismodelcanfullyextractcoarse-grainedglobalfeaturesandfine-grainedlocalfeaturesfrom
stocktimeseries.Atthesametime,theadaptivespatiotemporalgraphconvolutionallayerin
thismodelcanflexiblylearndynamicdependenciesbetweenindustries,companies,andtime
frames.
However,furtherin-depthresearchandanalysishavefoundthatdeterministicmethods
cannotfullyexplorethediversedatapatternsinlong-termstocksequences.Tolearnmultiple
datapatternsinlong-termstockdata,andimprovemodelefficiencyandeffectiveness,amore
robust(2)stockselectionmethodbasedonenhanceddiversevariationalauto-encoderwasex-
ploredanddesigned
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