Neural Net Stock Trend Predictor - Department of … · Neural Net Stock Trend Predictor By Sonal...

Neural Net Stock Trend Predictor

By Sonal Kabra

Advisor:Dr.ChrisPolle-

Commi,eeMembers:Dr.RobertChun

Mr.PaulThienprasit

SJSU Washington Square

•  Purpose•  Introduc7on•  ReviewofExis7ngWork

•  PriorExperiments

•  OurApproach•  NeuralNetworks•  ModelsDeveloped

•  Results•  Conclusion

Agenda


•  This project consisted of experiments and implementations of several neural nets to predict Stock Market movement and indicates whether the stock under study should be -- Bought, Neutral, or Sold to generate profit.

•  All of our neural nets were designed to predict stock prices for the following week.

Purpose


•  Since the beginning of the stock market in 1817 in the United States, accurate stock prediction has been a goal of investors.

•  One difficulty in accurately predicting stocks is the high

number of variables on which they depend.

Introduction


•  Our neural nets used financial data from Quandl. •  Below is some example data showing attributes this data

has.

Introduction


•  Mostinvestorsfollowtwoanaly7calmethods:

•  FundamentalAnalysis•  Studiescompanyfundamentalfactors•  Helpstheinvestorstofindthestocksworthinves7ng

•  TechnicalAnalysis

•  Iden7fiesthefutureuptrendordowntrendpa,erns.

Introduction


•  Stock Prediction is not a new concept. •  Kara et al. [1]

•  Twomodels:aneuralnetworkandanSVM,eachusedtopredictthedirec7onofstockpriceindexmovement.

•  BothuseISENa7onal100Indexforthedataset•  Bothuseatotalof10technicalanalysisindicators•  Theneuralnetworkhadanaccuracyof75.74%and

theSVMhadanaccuracyof71.52%.

Review of existing work

SJSU Washington Square Our Approach

•  Instead of predicting Up/Down signals, it will predict stock trade signals namely “Buy, Sell or Neutral” for next week.

•  Instead of combining all the technical indicator, neural net will train separately for each indicator.


•  Whenever human invests in stocks, they try to study the past data to find the similar pattern.

•  The earlier experiments used K-nearest neighbor and decision tree machine learning regression techniques.

•  By using those techniques, earlier experiments predict the

closing price of the same day.

Prior Experiments


•  Thealgorithmstatesthatthepredic7onvaluesaresimilarfortheobjectsthatareincloseproximityofeachother.

•  Thus,wecanassumethatthepredic7onvalueswillbealmostequalforsuchobjects.

K-nearest neighbor: Prior Experiments


•  KNN has a high error range for both stocks •  The predictions are completely off from the right prices.

ActualPrices:FB:149.78CSCO:34.3

K-nearest neighbor: Prior Experiments


•  Helpstomakepredic7onsbymappinggivenobserva7onstoconclusions.

•  Dividetheinforma7onintosmallgatheringsbasedonmaximizinginforma7ongain.

ActualPrices:FB:149.78CSCO:34.3

Decision Tree: Prior Experiments

OurApproach

Technical Analysis for stock prediction

•  Effec7veforshort-termtrading.

•  Observesmoneyflow,momentum,andvola7lity.

•  Supplementsinconfirma7onoftrendorpa,ern

•  2Types:– LeadingIndicators– LaggingIndicators

Moving Average Crossover

•  Isalaggingindicator.•  Formula:

•  5-Dayand10-DayMovingAverageCrossoverStrategy

Relative strength Index

•  Itisaleadingindicator•  Ittellswhetherthegivenstockisoverboughtoroversold.•  Formula:

•  Theprojectisusingthe14-DayperiodfortheRSI.•  TheRSIvalueabove70indicatesanoversoldregion,while

below30indicatestheoverboughtregion.

On Balance Volume

•  Itisusedtofindbuyingandsellingtrendofthestock.•  Itcalculatestheposi7veandnega7veflowofthevolume

onitsprice.•  Ifthecurrentclosingpriceismorethanthepreviousclose

price:

•  Ifthecurrentclosingpricefallsbelowthepreviouscloseprice:

•  ElseitwilljustassignthepreviousOBVtocurrentOBV.

Artificial Neural Network

•  Arecomputa7onalmodelsthatreplicatethebehaviorandadaptthefeaturesofbiologicalneuralsystems.

•  Hasthousandsofar7ficialneuronsjustlikethehumanbrainhasneuronnodes.

Artificial Neural Network

•  InputlayercomprisesofnodesorallinputfeaturesintheTrainingset.

•  Hiddenlayercomprisesofthenoderesponsibleforthe

processingandlearningofdatafromtheInputLayer.•  Outputlayercomprisesofaclassnode.

Training Neural Network

•  Backpropaga7onalgorithm.

•  Theproblemissetupasminimiza7onofalossfunc7on.

•  RPROPAlgorithm

Libraries Used

●Keras(NeuralNetworkLibrary)

●Sklearn(Machinelearninganddataanalysislibrary)

●Numpy(formathema7calcalcula7ons)

●Matplotlib(Ploengtheresults)

●QuandlAPI(Stockdata)

●Pandas(storingstockdatastructure)


•  S&P500market

•  Blanketsadiversesetofmul7na7onalcorpora7ons

•  CollectthedatasetfromQuandl.

•  PythonQuandlAPI

Dataset


•  All the features in the data set are not in similar range.

•  Thevaluesindatasetsarenormalizedintherangeof[-1,1].

•  The formula is:

Data Preprocessing


•  Thedataispar77onedintothetraining(70%ofthedataset),thevalida7on(20%)dataset,andtest(10%)

•  The100con7guousdatapointsarerandomlyheldfromthegenerateddataset.

•  Theneuralnetistrainedonaround800stockdatapointsandlatertestedon100.

Data Preprocessing


•  4-layerneuralnetwork.

•  30inputnodes:Threenodesforeachday7lltendays.

•  Inputfeatures:5-DaySMA,10-DaySMAandClosingpriceofthatday.

•  2hiddenlayers:60and60.

•  Theac7va7onusedistanh.

Moving Average Crossover Model



•  20inputnodes:Twonodesforeachday7lltendays.

•  Inputfeatures:14-DayRSI,andClosingpriceofthatday.



RSI Model



•  30inputnodes:Threenodesforeachday7lltendays.

•  Inputfeatures:Onbalancevolumeoftheday,Volumeoftheday,andClosingpriceofthatday.



OBV Model


•  Allthemodelsaremergedintofinallayeroftheneuralnetworkasshowninthefollowingfigure.

•  Thewholearchitectureistrainedtogether,insteadoftrainingeachmodeldifferently.

Merged NN Randomized Model


•  Thearchitectureissameaspreviousmodel.

•  Thetestdatasetgeneratedinthisexperimentisnotrandom.

•  Thetrainingisstrictlyforcedtousetheearlydaysofstockdata,andtes7ngisdoneinrecentdaysofstockdata.

Merged NN in Sequence Model


•  Asthisisamul7-classifica7onproblem("Buy,""Sell,"or"Neutral"),theaccuracymetricusedisaConfusionMatrix.

•  Accuracydefinedisthenumberofcorrectlyclassifiedpointsincomparisontothetotalnumberclassifica7onsmade.

Results

TotalnumberofObserva7ons

True_Buy+True_Sell+True_NeutralAccuracy=


•  Tocalculatetheprofitabilityforeachmodelfollowingformulaisusedtocalculatethenormalizedweeklyreturnofastock.:

•  Theaveragerisk-freerateofweeklyreturnis0.035%

Results

(Total_Posi7ve+Total_Nega7ve)–(False_Posi7ve+False_Nega7ve)

totalobserva7onsReturnThreshold(=1%)*


C[Acc.47.00%(+/-4.16%)]2.72HD[Acc.37.60%(+/-3.47%)]

1.04

RSI Model

XOM[Acc.29.60%(+/-8.16%)]

0.71

CAT[Acc.37.40%(+/-6.41%)]

1.87

RSI Model

OBV Model

C[Acc.46.80%(+/-4.58%)]

1.16 HD[Acc.34.20%(+/-1.16%)]0.77

OBV Model


CAT[Acc.44.36%(+/-4.76%)]

8.5 HD[Acc.34.26%(+/-1.76%)]0.90

Merged NN In Sequence Model

CAT[Acc.37.53%(+/-3.72%)]

1.21 AAPL[Acc.26.22%(+/-5.88%)]0.22

Merged NN In Sequence Model

Results

Whysomemodelsmayperformedpoorly:

•  ModelComplexity

•  TrainingData

•  MarketNoise

Conclusion

•  FromtheConfusionmatricesforabovesimula7ons,MergedModelRandomizeds7llgivesbe,erresultsthantheMergedModelinSequence.

•  Ifweconsideronlymovingaveragecrossovermodel,thenthatmodelgivesmorereturnsthanrestofthem.

•  Therefore,forfuturedevelopmentonecansurelyuseMovingaveragecrossovermodel.

References

•  YakupKara,MelekAcarBoyacioglu,andÖmerKaanBaykan.Predic7ngdirec7onofstockpriceindexmovementusingar7ficialneuralnetworksandsupportvectormachines:Thesampleoftheistanbulstockexchange.ExpertsystemswithApplica2ons,38(5):5311–5319,2011.

•  ShunrongShen,HaomiaoJiang,andTongdaZhang.Stockmarketfore-cas7ngusingmachinelearningalgorithms,2012.

•  E.F.Fama,K.R.French,"Commonriskfactorsinthereturnsonstocksandbonds",Journaloffinancialeconomics,vol.33,no.1,pp.356,1993.

References

•  D.G.McMillan,"Stockreturndividendgrowthandconsump7ongrowthpredictabilityacrossmarketsand7me:Implica7onsforstockpricemovement",Interna2onalReviewofFinancialAnalysis,vol.35,pp.90101,2014.

•  M.Billah,S.WaheedandA.Hanifa,"Stockmarketpredic7onusinganimprovedtrainingalgorithmofneuralnetwork,"20162ndInterna2onalConferenceonElectrical,Computer&Telecommunica2onEngineering(ICECTE),Rajshahi,2016,pp.1-4.

•  M.D.Godfrey,C.W.Granger,andO.Morgenstern,"Therandom-walkhypothesisofstockmarketbehaviora,"Kyklos,vol.17,no.1,pp.1-30,1964.

•  J.Murphy,"Technicalanalysisofthefinancialmarkets,pren7cehall,london,"1998

THANKYOU..!!!

Neural Net Stock Trend Predictor - Department of … · Neural Net Stock Trend Predictor By Sonal...

Documents

Transcript of Neural Net Stock Trend Predictor - Department of … · Neural Net Stock Trend Predictor By Sonal...