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1、SentimentPredictabilityforStocksJordanProsky1,AndrewTan2,MichaelZhao3,XingyouSong4Abstract—Inthiswork,wepresentourfindingsandex-VIConclusions8perimentsforstock-marketpredictionusingvarioustextualsentimentanalysistools,suchasmoodanalysisandeventVIIAPPENDIX9extraction,aswell
2、aspredictionmodels,suchasLSTMsandspecificconvolutionalarchitectures.References9CONTENTSI.INTRODUCTIONIIntroduction1Stockmarketpredictionisanimportanttime-serieslearn-ingprobleminfinanceandeconomics.AccordingtotheIIRelevantandPreviousWorks1semi-strongformoftheefficientmarketh
3、ypothesis(EMH),II-AAggregateTwitterPredictionofDowmarketsareefficientandstockpricesalreadyreflectallpub-Jones..................2liclyavailableinformationsuchasfinancialnewsandpriceII-BStockMovementPredictionUsingdata.Thishypothesisincorporatestheweak-formEMH,NewsArticleTitle
4、swithSVMs...2whichimpliesthatstockpricesreflectallmarketinformation,II-CDeepLearninginFinance.......2asubsetofpublicdata.Semi-strongformEMHassumesIIIIndividualStockPrediction2thatthemarketadjustsquicklytoabsorbnewinformationIII-ADifficulties...............2andstockpricesrefl
5、ectallnewavailableinformation.GivenIII-BAdvancedTextualInformationAp-thisassumptionandthefactthatinvestorspurchasestocksproaches................3afterpublicinformationisreleased,aninvestortheoreticallycannotbenefitoroutperformthemarketbytradingonnewIVExperiments3informatio
6、n.Severalstudiescontradictthishypothesis,andIV-AStockPriceandFinancialNewsData3throughourresearch,weshowthatweareabletooutperformIV-A.1WebsitesUsed.......3themarketusingacombinationofdeeplearning,temporalIV-A.2SentimentAnalyzer....3data,andnewsarticles.IV-A.3OpenIEExtract
7、orandOurreportfocusesontheuseofvarioustime-seriesandWord2Vec.........3NLPtechniquestoextractinformationaboutanindividualIV-A.4DeepLearningPackages.3stock’schanceofclosinghigherorlowerthanitsopeningIV-BSetup..................3price.WetakeadvantageofthetemporalnatureofbothI
8、V-B.1LSTM-BasedMoodPre-stockpricedataandfinancialnewsdata.InSection2,wediction...........3explore
