12th International Conference on Information & Communication Technology and System (lCTS) 2019

Stock Composite Prediction using NonlinearAutoregression with Exogenous Input (NARX)

Claudia PrimasiwiDepartment ofInformatics

Institut Teknologi Sepuluh NopemberSurabaya, Indonesia

claudiaprimasiwi@gmail.com

Riyanarto SarnoDepartment ofInformatics

Institut Teknologi Sepuluh NopemberSurabaya, Indonesiariyanarto @i£its .ac.id

Cahyaningtyas Sekar WahyuniDepartment ofInformatics

Institut Teknologi Sepuluh NopemberSurabaya, Indonesia

cahyaningtyas. I 8051@mhs.its.ac.id

Kelly Rossa SungkonoDepartment ofInformatic s

Institut Teknologi Sepuluh NopemberSurabaya, Indonesia

kelly@its.ac.id

Abstract- The stock composite cost is utilized as a markerto tell the presentation of the recorded open organizations. Thepast stock costs in the financial exchange can be utilized foranticipating the future cost of the stock. Because of the snafucircumstances of the stock value, the best-performed forecastmodel remains a test. This investigation study predicts the stockcomposite cost by utilizing Nonlinear Autoregression withExogenous Input (NARX). This technique is contrasted and theNeural Network model. In light of the examination, the NARXmodel outcome has moderately lower Mean Squared Error(MSE) esteem than the Neural Network model outcome. Thelittle MSE of NARX model is 4.2, which is acquired by fivedeferrals and six neurons. Notwithstanding, the most reducedMSE of the NN model is 4.62 by utilizing ten neurons.

Keywords- Bayesian regularization, forecasting, NARX,stock composite price

I. INTRODUCTION

As indicated by the Capital Market Law 8 - 1995, the fundtrades are described as "exercises that are identified with thePublic Offering and Securities Trading, Public Companies,and those worried about Surveillance, which is in colleges ororganizations.". In the capital market, the development ofstock costs can be seen from the stock value record. Inferringout of the stock value list, the financial specialists can knowthe example of the stock improvement, paying little respect towhether it is climbing, down, or stable [I], IDX (IndonesiaStock Exchange) Composite, some time ago known as JSXComposite, or otherwise called IHSG (Index Harga SahamGabungan) in Bahasa contains the list of all stocks recordedon the Indonesia Stock Exchange (IDX) with setting up on 10­08-1982 and 100 prediction list. As of December 2018, thequantity oforganizations recorded in IDX is in any event 615organizations [21. The quantity of the list is as of nowexpanding as the modem development increments and moreorganizations sell their stock openly on IDX.

The dynamic development of IDX is broadly utilized as apointer to demonstrate the presentation of the organizationsrecorded in IDX [2l. When, the past stock rates in the financialexchange can be spared and utilized for sometime later.Consequently understanding and foreseeing the conduct ofthestock is required by the financial specialists bothhypothetically and for all intents and purposes. As indicatedby the financial perspective, the change of items, stocks,

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records, and monetary forms will in general connotedistinctive conduct. For instance, the working of financialexchanges is influenced by subjective factors, for example,political, monetary, and common variables, among numerousothers. Interestingly, as indicated by the numericalperspective, they share a few similitudes, explicitly the changeofthe incentive after some time [3]. The capacity ofpast stockcosts goes for some time later [3].

With the assistance of a PC in information mining, theforecast of things to come conversion scale is conceivable.Assurance of the gauge is by estimating strategies [41-[61. Inthe interim, giving an increasingly noteworthy achievement ofrate developments is testing. The current conventional relapsemodels, for example by utilizing ARIMA (Auto-RegressiveIntegrated Moving Average) or GARCH (GeneralizedAutoregressive Conditional Heteroskedasticity) techniquepredicts the developments of the rate dependent on the directpresumptions of information [7l. In any case, in the capitalmarket, the record costs are notable for its trouble to foreseeutilizing the conventional anticipating techniques due to theirclamorous nature [8].

There have been several prior studies done to predict thealternation of the exchange rate [91-[131. One of the mostimplemented methods in currency applications is the NeuralNetwork (NN). Neural Networks offers a new platform toexplore the dynamics of various financial challenges.Therefore this method has been applied widely in the business[141-[161 studies such as in sales retail and marketingprediction, risk assessment prediction, and financial marketprediction [17].

Generally, The features ofNeural Network (NN) are staticand dynamic. Static networks are known for not havingfeedback elements and delays. The output of the static systemis calculated straight from the current inputs. The staticnetworks assume that the data is concurrent and have no senseof time, which results in spontaneous behaviour. Whiledynamic systems are harder to train, these networks are knownto be more potent than static networks. The dynamic networkshave a sort of memory in the form of delays which can betrained to learn sequence data or time-varying patterns. Thatbeing said, the dynamic networks are suitable for variousapplications such as financial predictions, speech recognition,fault detection, and many more [18] .

This paper proposes a stock composite prediction based onthe Indonesia Stock Exchange (IDX) by using NonlinearAutoregressive Exogenous or External Model (NARX) . Thisstudy will consider using Nonlinear AutoregressiveExogenous or External Model (NARX) due to its ability toforesee the developments of the securities exchange. NARXis one kind of artificial neural network system which issuitable to be applied to time series model regression [I 91­NARX networks use taps to set up the delays across the inputsand also incorporates the past values of the output.

II. RESEARCH METHOD

In this study, the first step to do is to select a network. Inthis case, NARX is chosen. After the network model isselected, the data will be trained using BayesianRegularization algorithm. Finally, the performance of theselected method will be evaluated by comparing predictionvalues by using NARX and those by using Neural Network.Each step in this study is depicted in Fig. 1.

TABLE I. JKSE HISTORICAL PRI CE ON YAHOO FINANCE.

Date Open High Low Volume Adj Close

05/01/2017 5302632813 5328490234 5302632813 77219400 5325503906

06/01/2017 5321745117 5350245117 5318384766 71615300 5347021973

09/01/2017 5350879883 5360061035 5307584961 97010200 5316363770

10/01/2017 5330015137 5331132813 5292062988 70792500 5309923828

11/01/2017 5316151855 5322979004 5301236816 84703100 5301236816

12/01/2017 5301036133 5322419922 5292750000 100764000 5292750000

13/01/2017 5299050781 5310419922 5272982910 70293600 5272982910

14/01/2017 5283618164 5291496094 5270011230 73077600 5270011230

14/01/2017 5266922852 5287124023 5265602051 86901800 5266937988

Comparing t heresults by using

MSE

Stoc kPrediction

Price obta inedby NARX

StockPrediction

Price obta inedby NeuralNetworks

Processing t heData by using

NARX

Processing theData by using

Neural Networks

Data of IndonesianStock Exchange

(Open price, Highprice, Low price,

Closing price,Transaction

vo lume)

Fig. 1. Pipeline of This Study

Hidden LayerOutput

Fig. 2. The Architecture ofNARX Model.

44

MSE Delavs(value x el4 ) 2 3 4 5

Hidden 2 7.72 7.95 16.1 6.10Neurons 3 6.91 4.66 19.8 7.8

4 6.32 4.28 18.9 14.05 7.04 11.1 17.5 13.06 6.09 4.80 10.0 4 .207 4.68 10.1 15.4 8.68 6.44 5.56 10.1 5.189 8.52 8.37 18.7 16.110 7.92 7.56 13.0 14.2

Stock Prediction NARX Real Stock(predicted Value) (Targeted Value)

5321800000 5325503906

5335700000 5347021973

5359200000 5316363770

5331800000 5309923828

5326900000 5301236816

5311200000 5292750000

5310200000 5272982910

5298900000 5270011230

5293200000 5266937988

TABLE II.

TABLE III.

STOCKPREDICTION BYNARX AND REALSTOCK

THE MSE VALUEOFEACHDELAYS AND HIDDENN EURONS USINGNARX METHOD

A. Stock Composite Price DatasetThis study utilizes the data provided by Yahoo! Finance

under the symbol of J\ JKSE r20l Considering that the stockprice is seasonal, the data that will be used should be at leastin the past one-year long. In this study, the dataset will becollected from January 2017 to December 2017 and presenteddaily. Briefly, the data will be shown as in TABLE I. Fromthis data, we will be able to get the date, the open price index(open), the biggest transaction result of the day (high), thesmallest transaction of the day (low), the adjusted closingprice (adj close), and the transaction volume (volume) r211 .Unfortunately, certain days are not recorded because thecessation ofIndonesia trade. Therefore, the cleaning data fromthe vacancy is the beginning step in this process.

Data features of this study is the prices ofopen, high, low,and volume . The adjusted closing price is the target databecause the adjusted closing price serves a purpose as anevaluation for the future price .

B. Nonlinear Autoregressive Exogenous Model (NARX)In order to predict future values, a kind of dynamic

filtering is needed to process the available past values .Generally, dynamic neural networks are used for nonlinearfiltering and prediction [18].

The Nonlinear Autoregressive with External ExogenousInput (NARX) will be utilized in this study in order to predictthe future values of the stock price by utilizing the time seriesfrom past values Yet) and the second time series from the pastvalues x(t). The equation of NARX model is written as inEquation (l).

6.2

" I;E'"I-'tl§ S.8

"50."50 5.6

S.J

Response of Output Elemenl1 for Timc-Bertcs 1

Tm inng Tcrg::b- ":'r.ain n g CIlJ~ U:s

Valid;l tion Ta-ge:s- V:l~rhtinn n rtp lt...

t est rarcets- l est OJ IpJts

--Errors--R~J-Iu l l:lt:l

5.2 L-__---L- L-__----'-- --'---__-----' ---'--- -'-----__-----'- --'--- ---'--__---'

O :~· ·,01 I I 1 ~.~~~ I~. , T'''l'b -O~'''~ 1. I." I I I'I " " "' i, l lh; •... Ii i ,II" ~-,:"'111 1' " '" ''' ' ,"" f' 'I I~'I 'I'~

zo 4') 6C so 100 120 14') 16<: 1&) 200 U ')

Time

Fig . 3. The Example of Plot Response of Hidden Neurons = 10 and Delays = 2.

45

Outwardly, the engineering chart of the Nonlinear Autorelapse Exogenous Model can be found in Fig. 2, where a two­layer feed forward system is utilized for the estimation. The win Fig.2 is the outer info, y is the yield which is the forecastcost of stock composite, w is the weight.

taCt) = f(ti(t - 1), ti(t - 2), ... , t2(t - d), (1)tot: - 1), tott - 2), .... , tott - d)

where :f : non-linear functionti(t) : input ofNARXtaCt) : input and feedback ofNARXd : delays

C. Bayesian Regularization AlgorithmIn the wake of structure the system design, the

preparation calculation will be picked. This investigation isled with Bayesian Regularization Back proliferation orotherwise called Bayesian Regularization Algorithm for short.Bayesian Regularization calculation for the most part resultsin great speculation for little, boisterous, or troublesomedatasets . The preparation procedure will be stopped byversatile weight regularization. The Bayesian Regularizationworks successfully by evacuating the system loads whichdon't significantly affect the system model. BayesianRegularization keeps away from part ofpreparing informationto be utilized as approving information which results inpointless needs of cross-approval. In addition, BayesianRegularization keeps the system from both over-preparing andover-fitting issues [22].

So as to do the preparation procedure, the informationwill be isolated arbitrarily into preparing, approval, and testinginformation, which will be comprised of 80%, 10% and 10%from the underlying information individually. While thepreparation information is utilized to the system during thepreparation stage, the approval information is used to evaluatethe system speculation [23]. By utilizing approvalinformation, the framework will probably end the preparationwhen the speculation quits indicating improvement [24].Finally, the testing informatio will be utilized to gauge thesystem execution freely both during and after the preparationprocedure.

D. Forecasting EvaluationThe part will quantify the presentation of the constructed

model by figuring the Mean Squared Error (MSE). The meansquares blunder will be determined so as to discover theexactness estimation of the model [25]. The mean squaredmistake is the normal squared distinction between the yieldesteems and the objective qualities . The lower the estimationsof the MSE, the better the forecast model assembled [4]. TheMean Squared Error can be registered utilizing Equation (2).

MSE#Hidden Neurons (Value x e14 )

2 5.403 6.204 7.785 6.356 6.527 5.648 6.249 4.8210 4.62

MSE VALUE OF N EURAL N ETWORK MODEL OF E ACHHIDD EN N EURONS.

TABLE IV.

III. RESULT AND ANALYSIS

At first, the NARX system will be characterized bydeciding the quantity of shrouded neurons and the quantity ofdefers d. In this examination, the analyses will be led byutilizing 2 to 10 quantities of shrouded neurons while thequantity of the postpones will be indicated beginning from 2to 5 to locate the best performed concealed neurons and defersmix. The system will be made and prepared in open circlestructure. The open circle (single-step) is more favoured thanthe shut circle (multi-step) preparing because of itsbelongings. The open circle preparing makes providing thesystems with right past yields conceivable as the preparationprocedure is running so as to create the right current yields.The system might be changed over to either circle structure orwhatever other structure which required by the applicationafter the preparation.

As referenced over, the preparation, approval, and testinginformation will be separated into 80%, 10%, and 10%individually, which results in 190 columns of preparinginformation, 24 lines of approval information, and 24 lines oftesting information. Next, the estimating assessment of eachshrouded neurons and postpones investigations will befinished by figuring the mean square mistake esteems . Themodel examination between the outcomes by NARX and theobjective qualities is appeared TABLE II, in the interim, theconsequence of the MSE count can be found in TABLE III.Hypothetically, the lower the MSE esteem the better thesystem. In light of this examination, the most minimal MSEworth is 4.2 which accomplished by utilizing the quantity ofshrouded neurons = 6 and the quantity of deferrals = 5. Inopposite, the most elevated MSE worth is 1.9 whichaccomplished by utilizing concealed neuron = 3 and deferrals= 4. In view of this trial, the system model gets the bestoutcome with six concealed neurons and five deferrals . Thisimplies the NARX model can anticipate JKSE cost for as longas 5 days ahead with 4.2 mean square mistake esteem. Fig. 3plots the focused on time arrangement (targets) and the yieldtime arrangement (yields) on a similar hub. The plot reactionlikewise outwardly plots the mistake estimations of theobjectives and the yields.

Contrasted with the neural system technique in TABLEIV, the MSE estimation of each concealed neurons ismoderately higher than the MSE estimation of the NARXmodel. This demonstrates the outside data which gave in theNARX model adds to bring down MSE qualities to theNARX.

(2)m=l

MSE =~ '" (tVal _ yVal)2val M L m mM

M : the number of testing data

m : testing data index

t~al : targeted value

yi:tal : predicted value

where:

46

IV. CONCLUSION

The point of this examination is to anticipate theIndonesian stock composite cost. The forecast ofIndonesianstock composite cost is anticipated utilizing Nonlinear Autorelapse with Exogenous Input (NARX) by utilizing BayesianRegularization Algorithm for the information preparing.NARX results in lower Mean Squared Error (MSE) in theforecast model than the Neural Network model. As it were,the NARX beats the Neural Network model. Also, the NARXmodel can foresee as long as 5 days ahead with moderatelylower blunder esteems.

This study can be developed by using wide data andexecuting other forecasting methods to minimize the errors ofresults.

ACKNOWLEDGMENT

Institut Teknologi Sepuluh Nopember, The Ministry ofResearch, Technology and Higher Education of Indonesia,Direktorat Riset dan Pengabdian Masyarakat, andDirektorat Jenderal Penguatan Riset dan PengembanganKementerian Riset, Teknologi, dan Pendidikan TinggiRepublik Indonesia earn respects from the authors forsupporting the exploration.

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