INTRODUCTION Forecasting Stock Price Index Using Artificial Neural Networks in the Indonesian Stock Exchange.

CHAPTER 1 INTRODUCTION

Trade decisions on a stock market are made on the basis of predicting the trend which it is driven by many direct and indirect factors. Effective decisions depend on accurate prediction. Even though there have been many empirical researches which deal with the issue of predicting stock price index; most empirical findings are associated with the developed financial markets. There are few researches exist in the literature to predict the direct of stock price index movement in emerging markets, especially in Indonesian Stock exchange. Accurate predictions of movement of stock price indexes are very important for developing effective market trading strategies (Leung et al. 2000) the stock market is essentially dynamic, nonlinear, complicated, nonparametric, and chaotic in nature (Abu-Mostafa & Atiya 1996). In addition, stock market is affected by many macro economical factors such as political events, firms’ policies, general economic conditions, investors’ expectations, institutional investors’ choices, movement of other stock market, and psychology of investors etc. (Tan et al. 2007).

So far, there are many techniques are applied to predict the market in order to maximize profit and minimize risks, it can be categorized into four groups; these techniques are included fundamental analysis, technical analysis, time series analysis, and machine learning. The results from these techniques is different, for example the fundamental analysis is used to define models which Trade decisions oon a stock market are made on ththe basis of predicting the trend whhicich it is driven bby y mam nyny ddirecct and indirectt factors. Effective decicissions dependd on n acaccurate predictiion. EveEv n n ththouough there havve e been many empiricacal lreresesearchesswwhihich deal with the issueeoof fpredicctitingngsstotock priicece index; mo

mostst eempm iricicaal findings are associated with the develoopeped fifinanancnciaiall markrkets. Th

Theere arare few researches exist in the literature to predict ththe diirerectct of stocck pr

p icee index movement in emerging markets, especially in Indodonesisiann SStockk excchange. Accurate predictions of movement of stock price indeexes aarre vereryy immportant for developing effective market trading strategies (LLeung etet all. 20

20000) the stock mamarkrketet is essentially y dydynanamimic, nonlinear, cocommplicateted,d, nonparametric, and chaotic iin n naatuture (Abu-Mostafa & Atiya 19966).). IInn ad

addition, stock market is affected by many macro economical facttorors susuchch as po

p lilititicacall evevenentsts, , fifirmrms’s’ pololicicieies,s, ggenenereralal ecocononomimicc cocondndititioions, ininvevesstors’ ex

expepecttattiions,,ininsstitutional invevestors’ chohoiices, movemeentnt of fotthher sttocockk market, and psychology of investors eetc. (Tan ett al. 2007).

So far, there are many techhniques arare applied to predict the market in order to maximize profit and minimizee risksks, it can be categorized into four groups; these techniques are included fundamental analysis technical analysis time

calculate the future price according to current indicators such as the gross domestic product (GDP), consumer price index, interest rate and exchange rate; the technical analysis is used for forecast the future price direction by studying past market data - primarily stock price and volume. The technical analysis basically use trading rules such as single moving trend, composite moving trend, and channel breakout. However, these techniques don’t have ability to learn nonlinear variables as artificial intelligent (IA) approach does.

Artificial neural network (ANN) technique is one of data mining techniques that is gaining increasing acceptance in the business area due to its ability to learn and detect relationship among nonlinear variables. Also, it allows deeper analysis of larger set of data especially those that have the tendency to fluctuate within a short of period of time. If stock market return fluctuations are affected by their recent historic behaviour, neural networks which can model such temporal stock market changes can prove to be better predictors (Tang et al. 1991). The elasticity and adaptability advantages of the artificial neural network models have attracted the interest of many other researchers (Adebiyi Ayodele A. et al 2012). Since the last decade, the artificial neural network models have been used extensively in various branches such as computer science, engineering, medical and criminal diagnostics, biological investigation, analysing the business data, and econometric analysis research. Also they can be used for analysing relations among economic and financial phenomena, forecasting, data filtration, rate; the technical analysysisis iis used for ffororece ast the future price direction by studying past mamarket data - primarily stock price anand volume. The technical analysis bbasically use tradiinng rululess ssucuch h asas single movingng trend, composite moovving trend, annd d chchaannel breakout. Howeevever,r, tthehese techniqueess don’t have ability totolleaearnr nonlilinenear variables as artificialiintnteleligentt((IAIA) ) apa proachch does.

Ar

Arttificiiaal neural network (ANN) technique is onen of f dadatata minning te

techniqques that is gaining increasing acceptance in the businesss arereaa dudue e to itsts abilitity to learn and detect relationship among nonlinear variabbles. AAlsl o, it alloows deeper analysis of larger set of data especially those thhat have ththee tenndency to fluctuate within a short of period of time. If stockf mmarket rreturnrn flucctuations aarere afffected byby ttheir recenentt hhistoric bbehehavaviour, neurral netwoorkrkss which can model such temporal ststoco k market changes can prove to bee bbetetteer pr

prededicictotorsrs((Tanggeett alal..191 91).) The elasticityyaandndaadadaptptability y adadvavantagageses oof f the ar

artitficial neuuraral l nenetwororkk modedelsls hhavave attrracacteted ththe e ininterest of mamanyny other researarchchererss ((Adebiyi Ayodelee A. et al 2012). Since ththee lalastst decade, the artificial neural network mmodels havee been used extensively in various branches such as computer sciencee, engineering, medical and criminal diagnostics, biological investiggatioion, analysing the business data, and

generating financial time-series, and optimization (Shachmurove & Witkowska 2000).

Why neural network is best for prediction? Because there are several distinguished features that propound the use of neural network as a preferred tool over other traditional models of prediction. Artificial neural networks are nonlinear in nature and where most of the natural real world systems are non linear in nature, artificial neural networks are preferred over the traditional linear models. This is because the linear models generally fail to understand the data pattern and analyse when the underlying system is a nonlinear one. ANNs are data driven models. It has ability to discover nonlinear relationship in the input data set without a priori assumption of the knowledge of relation between the input and the output. The input variables are mapped to the output variables by squashing or transforming by a special function known as activation function. They independently learn the relationship inherent in the variables from a set of labelled training example and therefore involves in modification of the network parameters (Soni 2011).

It is of interest to study the extent of stock price index movement predictability using data from emerging markets such as of the Indonesia stock market.This emerging market’s performance is incredible for a long period of time. For example, in December 2002, composite index stand at 424.495 points, it was rapidly upwards to 2,745.826 at the end of 2007 and then decreased to 1,135.408 at the end of 2008 (-50.64%) due to the impact of global financial crisis and upwards again at the beginning of 2009 until Why neural netwoorkrk is best for predidictctioi n? Because there are several distinguished fefeatures that propound the use of neuralal network as a preferred tool oveverr other traditional momodedelsls oof f prprededicctitonon. Artificial neueural networks are noonnlinear inn naturree anand d where most of the natut raal l rer al wworld systeemsm are non linear inin nnatature, aartrtificial neural networks are prrefeferredd ovoverer tthe tradidtional liinenearar moddelels. This is because the linear models generalallyly faiil l toto uundersttana d th

the datata pattern and analyse when the underlying system is aa nononlilinenearar onee. A

ANNNs are data driven models. It has ability to discover nonlineaar relalatitiononship in tthe input data set without a priori assumption of the knowledgee of relatiionon bettween the input and the output. The input variables are mapped too the ooutpuut variriables byby ssququashing or trtransformimingng by a spececiaial functionn knownn aass activation function. They independndeently learn the relationship inherent inin tthhe va

v ririabableless from aa ssetet oof f labelled training g exxamamplplee and thererefefororee innvovolvlveses in mo

modification ooff ththee netwtworork pararamemeteterrs (Sonnii202 111)).

It is of interest to studydy the extxtent of stock price index movement predictability using data fromm emerging mmarkets such as of the Indonesia stock market.This emerging market’s’ perforrmance is incredible for a long period of time. For example, in Decembeer 22002, composite index stand at 424.495

November 2012, it reached 4,317. 277 points and 4,985.58 points on May 28, 2014. On an average day, transactions worth of between IDR 5 to 6 trillion (US $520 million to US $620 million), and it has an average daily volume of about six billion shares. In 2012, foreign investors purchased assets in the Indonesian capital markets totaled IDR 19.55 trillion (about US $2 billion). In the period of January to March in 2013, net foreign purchases already stood at IDR 18.5 trillion nearly 100% increase comparing to the previous year, which shows the attractiveness of these markets (David 2013). Market capitalization has increased 412% from IDR 801 trillion in 2005 to IDR 4,099.34 trillion - November 2012. In term of trade value, showed a significant increased after 2006 (US$ 49.4 billion) to US$114.9 billion in 2007 and quite stable since then i.e. eleven months of 2012 totaled of US$104.6 billion.

There are very little previous researches exist (accessible) in related to stock price prediction by using ANN techniques at IDX, such as Putra and Kosala (2011) used ANN to predict intraday trading signals and Veri and Baba (2013) forecasting the next closing price.

As we understand the characteristic that all stock markets, including IDX, have in common is the uncertainty, which is related with their short and long-term future state. This feature is undesirable for the investor but it is also unavoidable whenever the stock market is selected as the investment tool. The best that one can do is to try to reduce this uncertainty. In this research ANN model is proposed to forecast the movement of stock price in the daily IDX Index.

(US $520 million to US $$626200 million), anand dit has an average daily volume of about six billioonn shares. In 2012, foreign investororss purchased assets in the Indonesiianan capital marketsttotoalaededIIDRDR119..555 trillion (aboutut UUS $2 billion). In the e pperiod of Januuararyy toto March in 2013, net foforereigign nppurchases alrereada y stood at IDR 188.5.5 ttririllllion neeararlly 100% increase compariringng to the e prprevevioi us yeaar,r, which shhowowssththe atttrtractiveness of these markets (David 2013). MarkeM ket t cacapipitat lizaatiton h

has incrreased 412% from IDR 801 trillion in 2005 to IDR 4,4,099.3.344 ttrillion -No

N veember 2012. In term of trade value, showed a significant inincreaeasesed d after 20006 (US$ 49.4 billion) to US$114.9 billion in 2007 and quite stablel sinncece theen i.e. eleven months of 2012 totaled of US$104.6 billion.

There aree vevery little prrevevious reseaearrches exist t (a(accccessible) iin related d toto stock price prediction by using ANANN techniques at IDX, such as Puttrara aannd Ko

Kosasalala ((202 11)) ususededdd AANNN to predict intradayay ttraradidngg sigignanalsls aand Vd Vereri i aand Ba

Baba (2013) )foforerecacastinngg ththe nextxtcclolosising priicece..

As we understand the chaararacteristiic cthat all stock markets, including IDX, have in common is the uncerrtainty, whicch is related with their short and long-term future state. This featuree is unddesirable for the investor but it is also unavoidable whenever the stock mmararket is selected as the investment tool. The

1.1. Problem Identification

How to forecast the stock price index? The stock market index direction prediction is regarded as one of the crucial issues in recent financial analysis studies (Wang & Choi 2013). Many techniques are employed to predict stock prices in the stock markets but the past results are being questioned. Generally, there are three schools of thought in terms of the ability to profit from the equity market. The first school believes that no investor can achieve above average trading advantages based on the historical and present information. The major theories include the Random Walk Hypothesis and the Efficient Market Hypothesis (Peters 1991). The Random Walk Hypothesis states that prices on the stock market wander in a purely random and unpredictable way. Each price change occurs without any influence by past prices. The Efficient Market Hypothesis states that the markets fully reflect all of the freely available information and prices are adjusted fully and immediately once new information becomes available. If this is true then there should not be any benefit for prediction, because the market will react and compensate for any action made from these available information. The second school’s view is the so-called fundamental analysis. It looks in depth at the financial conditions and operating results of a specific company and the underlying behavior of its common stock. The value of a stock is established by analysing the fundamental information associated with the company such as accounting, competition, and management. The fundamental factors are overshadowed by the speculators trading.

prediction is regarded as onone of the crucicialal issues in recent financial analysis studies (Wang & & CChoi 2013). Many techniques are ememployed to predict stock prices in n the stock markkeets s bubut t ththee papast results are bbeing questioned. Geennerally, there aaree thrh ee schools of thoughght inin ttererms of the abbilility to profit from thehe eeququiity maarkrkeet. The first school believeess that noo ininvevestsor cannaachieve abbovovee averraage trading advantages based on the hihistsorici alal aandnd preeses nt infformatation. The major theories include the Random Walk HyHypothhesesisis and thhe Ef

E ficicient Market Hypothesis (Peters 1991). The Random Waalkl HHypypototheh sis stattes that prices on the stock market wander in a purely rrandom aandnd unnpredictable way. Each price change occurs without any influennce byy passt pr

pricices. The Efficiienentt MaMarkrketet Hypothesis ststatatesestthahatt ththe markets fullllyy reflect t aalll of the freely available informrmattioion and prices are adjusted fully y anandd im

immem diately once new information becomes available. If this is truuee ththenen tthehere sh

s ouldld nott bbe anany y bebenefit foforr prprededicictitioon, becaaususe e ththe markket will ret reacactt and coompmpensatetefforor any action maaded from ththese available ininfoformation.n.TThe second

school’s view is the so-calleed fundameental analysis. It looks in depth at the

financial conditions and opeerating resesults of a specific company and the underlying behavior of its commmon sstock. The value of a stock is established by analysing the fundamental inforo mation associated with the company such

Technical analysis assumes that the stock market moves in trends and these trends can be captured and used for forecasting. Technical analysis belongs to the third school of thought. It attempts to use past stock price and volume information to predict future price movements The technical analyst believes that there are recurring patterns in the market behavior that are predictable. In fact, there is not any research proved the existing of such patterns due to each stock market has different characteristics, depending on the economies they are related to, and varying from time to time. Most of the techniques used by technical analysts have not been shown to be statistically valid and many lack a rational explanation for their use. However, technical analysis has its value on forecasting.

Artificial Neural Networks are regarded by many as one of the more suitable techniques for stock market forecasting (Yao & Tan 2001). It has been demonstrated to be an effective technique for capturing dynamic non-linear relationships in stock markets, while technical analysis techniques unable to do so.

1.2. Objective of the Research

The core objective of this research is to forecast the direction of movement in the daily JKSE or IDX using artificial neural network, also to compare a financial performance model and statistical model of ANN on its precision of stock price prediction. At the same times, its empirical result will be comparing with some recently researcher’s works in this market that used ANN models. Also, this research will seek to prove against a validity of the the third school of thoughghtt. IIt attemptst ttoo use past stock price and volume information to pprredict future price movements The e tetechnical analyst believes that therere are recurring patteernns sininthethemmarkeket behavior that tara e predictable. In factct, there is notaanynyrrese earch proved the exististining g ofof such patternsns due to each stock mamarkrketet has ddififfeferent characteristics, depeendnding onon tthehe economimies they are e rerelalatted toto, and varying from time to time. Most of ththe techchniniququeses used dby technicah al analysts have not been shown to be statistically vallidid andd mmany lackck a

a ratitional explanation for their use. However, technical analysiiss has s ititss vav lue on fforecasting.

Arrtificial Neural Networks are regarded by many as one of the mmore ssuuitablle techniquess fforor sstotockck mmararket t fof recasttining g (Y(Yaoao && TTanan 22001). It has bebeenen demonstrated to be an effective tetechnique for capturing dynamic nonn-lilineneaar re

relal tiionnshshipipss inin sstotockck mmararkekets, while techchninicacall ananalalysysisis tectechnhniiquess ununabablee to do

do sso.

1.2. Objective of the Research

The core objective of this ressearch is to o forecast the direction of movement in the daily JKSE or IDX using g artificcial neural network, also to compare a financial performance model and ststatistical model of ANN on its precision of

Efficient Market Hypothesis and the Random Walk Hypothesis for short-term trading advantages in this stock market, which is considered as one of the most important emerging markets in Asia.

1.3. Contribution of the Research

Recently, a number of researchers have explored artificial intelligence techniques such as ANNs to solve financial problems significantly increased, but most has targeted the United States market (Suchira Chaigusin, 2011). There have been limited attempts to research stock markets of developing economies such as Indonesia. At the beginning of this research, the author find that there are some previous research using intelligent approach in this market, but there are not many existing research using artificial neural network technique, specifically, to predict the index movements of the JKSE.

The major contributions of this study are to demonstrate and verify the predictability of stock price index direction using the financial and statistical performances of ANN model. It also benefit to other researchers/students who are interested in studying stock market price movement with ANN model. This study is one step along the path towards applying ANN to the IDX in order to clarify and predict stock performances. Enhancing the use of ANN in financial areas and contributing incrementally to the growing knowledge base of this financial forecasting field.

most important emerging gmamarkets in Asik iaa.

1.3. Contributionn of the Researchc

Rececently, a nuumbmbeer of reeseseaarchers h s hahaveve eexpxplolorred artificialal intelligence t

techniququeses suuch as ANNNNs to solve financialal pproroblems sisigngnifificicantly inincreased, buut momosts hass targeted the United States market (Sucht chira ChChaiaigugusin, 22010 1). Th

Theere hahave been limited attempts to research stock markekets oof f dedevvelopinng ec

e onoomies such as Indonesia. At the beginning of this research, tht e auauththoror findnddddd thatt there are some previous research using intelligent approach inn thissmmarkeett, buutt there are not many existing research using artificial neurral netwworkk te

techchnique, specifically,y, to ppredict the index movements of the JKSEE..

The major contributions of thihis s ststudy are to demonstrate and verifify y ththe pr

p ededicictatabilityy of stock prp ice index direction using g the financiaiall anandd ststatatisstitical pe

performances ooff ANANN model. IIt t alalsoso bebenefit to oththerer rrese earchers/stutudedentnts who are ininteterereststeded in studying stockk market t price movement wwitithh ANANNN model.

This study is one step alongg the path ttowards applying ANN to the IDX in order to clarify and predict stoockc perfoormances. Enhancing the use of ANN in financial areas and contributing iincn rrementally to the growing knowledge base

1.4. Scope of the Research

As there are many ANN research techniques were used to predict stock price movement index mentioned in 1.1; some of recently research are using international market indicators, technical & fundamental indicators as inputs which it’s called “hybrid”. But some try to combined a lot of indicators as input variable. However, the research finding indicated that many indicators/ or higher number of input variables is not mean such forecasting technique (model) will give a result more accuracy.

In this research, we will attempt to forecast daily stock price movement at JKSE, using financial performance and statistical performance evaluations of ANN models. The data collection with total period of 9 years and 5 months, starting from January 3, 2005 to May 28, 2014. The reason we start from 2005 because the index prices at the beginning 2005 reached RP1.000 and has dramatically increased until the end of 2007 and then in the direction of decrease. At the beginning of 2009 index prices had in the direction of increased again. So, we would like to see how ANN model perform in the different period and direction. The data be divided into two separate sets, a period of January 3, 2005 to December 30, 2010 is for network training purposes. From January 3, 2005 to May 28, 2014 is for testing the predictive ability of the network. A three-layered feed-forward ANN model will be constructed (see Figure. 2). This ANN model consists of an input layer, a hidden layer and an output layer, each of which is connected to the other. Inputs for the network are twelve technical indicators which are represented by twelve neurons in the input layer (see table 2).

movement index mentioneded iinn 11.1;1; ssomo e of recently research are using international markrkeet indicators, technical & fundadamem ntal indicators as inputs

which it’ss called “hybrid”. But some try to combined aa llot of indicators as

inpuutt variable. HoHowew vever, thee rese earch h fifinddinngg inindidicacated that manany indicators/ or higheer r nunumbm er of ininpput variables is nott mmeae n such ffororececasting ttece hnique (mmododelel))will ggivive a result more accuracy.

In thhisis research, we will attempt to forecast daily stock pprircemmovoveme ent ata JK

J SEE, using financial performance and statistical performance evalluau titionons off ANNNN models. The data collection with total period of 9 years annd 5 mmonthshs, staarting from January 3, 2005 to May 28, 2014. The reason wee start frfromm 20

2 005 because the index pprices at the beginningg 2005 reachedRP1.000000 and hhasas dramatically increased untild l tthehe endnd oof 2007 and then in the directionon oof de

d crease. At the beginning of 2009 index prices had in the dirireectitionn of incrcreaeasesed d agagaiainn. SSo,o, wwee wowoululd d lilkee ttoo seseee hohow w ANANNN momodedel l peperfrforrm m inn the di

diffffererenentt peperiodod aand directionon. The daatata be dividedd ininto ttwowo ssepepararatate sets, a period of January 3, 2005 tto Decembber 30, 2010 is for network training purposes. From January 3, 20005 to Maayy 28, 2014 is for testing the predictive ability of the network. A threee-layeyered feed-forward ANN model will be constructed (see Figure. 2). This AANN model consists of an input layer, a

The forecasting ability of the ANN model is accessed by using back-propagation neural network of errors such as MAE, RMSE, MAPE, and R2, to improve their prediction performances two comprehensive parameter setting experiments for both technical indicators and the levels of the index in the market are performed. The logistic sigmoid transfer function will be used in neural network; this function converts an input value to an output ranging from 0 to 1 (see 2.3). If the connection weight is negative or (value < 0) then tomorrow close price value < than today’s price (loss). If the value is positive (value > 0.5) then tomorrow close price value > than today’s price (profit). As we want to see the direction of movement decrease or increase so the output will be categorized as 0 and 1. Therefore, if the forecasting value smaller than 0.5 it will be categorized as decreased direction.

Published stock data will be collected from Yahoo.Finance especially daily closing price, daily high price, and daily low price of total price index.

1.5. Organization of the Thesis

This thesis is composed of six Chapters. The remaining portion of the thesis is broken up into the following Chapters: Chapter 2 describes Artificial Neural Network and related literature. Chapter 3 describes the research methodology which includes ANN modeling, research data and technical input variables. In Chapter 4 descriptive statistics are used simply to describe the sample. Chapter 5 the empirical results are summarized and discussed. Chapter 6 brief conclusion with some suggestions for the future work on the field of stock market prediction

improve their prediction ppererfformances twtwo o comprehensive parameter setting experiments for r bboth technical indicators and the lelevels of the index in the market aarere performed. The lol gigststicc ssigigmomoidid transfer functitiono will be used in neurural network; ththisis ffunu ction converts an ininpuput t vavalue to an ouutptput ranging from 00ttoo 11(s(see 2.33)). If the connection weighthtiissnegatiiveveoor r (value << 0) then tomomorrrroow clolose price value < than today’s price (loss). IfIf the valalueue iis sposisitive (

(value >l > 0.5) then tomorrow close price value > than todayy’s prricice e (profit)t). As

A wwe want to see the direction of movement decrease or inncreaasese ssoo the outtput will be categorized as 0 and 1. Therefore, if the foreccasting valuluee smmaller than 0.5 it will be categorized as decreased direction.

Pubblished stocockk ddata will bebe collectedd ffrom Yahoh o.oFiFinan nce espep cially ddaiailyly closing price, daily high price, andndddaily low price of total price index.

1.

1.5.5. OrOrgaganinizazatitiononooff ththee ThThesesisis Th

Thisis thesis isisccomompoposesedd ofofssix ChChapapteters. TTheherrememaininingng pporo tion ooff ththee ththesis is broken up into the following CChapterrs:s Chapter 2 describes AArtificial Neural Network and related literaturre. Chapter 3 describes the research methodology which includes ANN modelingg, researcch data and technical input variables. In Chapter 4 descriptive statistics arre used simply to describe the sample.

November 2012, it reached 4,317. 277 points and 4,985.58 points on May 28, 2014. On an average day, transactions worth of between IDR 5 to 6 trillion (US $520 million to US $620 million), and it has an average daily volume of about six billion shares. In 2012, foreign investors purchased assets in the Indonesian capital markets totaled IDR 19.55 trillion (about US $2 billion). In the period of January to March in 2013, net foreign purchases already stood at IDR 18.5 trillion nearly 100% increase comparing to the previous year, which shows the attractiveness of these markets (David 2013). Market capitalization has increased 412% from IDR 801 trillion in 2005 to IDR 4,099.34 trillion - November 2012. In term of trade value, showed a significant increased after 2006 (US$ 49.4 billion) to US$114.9 billion in 2007 and quite stable since then i.e. eleven months of 2012 totaled of US$104.6 billion.

There are very little previous researches exist (accessible) in related to stock price prediction by using ANN techniques at IDX, such as Putra and Kosala (2011) used ANN to predict intraday trading signals and Veri and Baba (2013) forecasting the next closing price.

As we understand the characteristic that all stock markets, including IDX, have in common is the uncertainty, which is related with their short and long-term future state. This feature is undesirable for the investor but it is also unavoidable whenever the stock market is selected as the investment tool. The best that one can do is to try to reduce this uncertainty. In this research ANN model is proposed to forecast the movement of stock price in the daily IDX Index.

(US $520 million to US $$626200 million), anand dit has an average daily volume of about six billioonn shares. In 2012, foreign investororss purchased assets in the Indonesiianan capital marketsttotoalaededIIDRDR119..555 trillion (aboutut UUS $2 billion). In the e pperiod of Januuararyy toto March in 2013, net foforereigign nppurchases alrereada y stood at IDR 188.5.5 ttririllllion neeararlly 100% increase compariringng to the e prprevevioi us yeaar,r, which shhowowssththe atttrtractiveness of these markets (David 2013). MarkeM ket t cacapipitat lizaatiton h

has incrreased 412% from IDR 801 trillion in 2005 to IDR 4,4,099.3.344 ttrillion -No

N veember 2012. In term of trade value, showed a significant inincreaeasesed d after 20006 (US$ 49.4 billion) to US$114.9 billion in 2007 and quite stablel sinncece theen i.e. eleven months of 2012 totaled of US$104.6 billion.

There aree vevery little prrevevious reseaearrches exist t (a(accccessible) iin related d toto stock price prediction by using ANANN techniques at IDX, such as Puttrara aannd Ko

Kosasalala ((202 11)) ususededdd AANNN to predict intradayay ttraradidngg sigignanalsls aand Vd Vereri i aand Ba

Baba (2013) )foforerecacastinngg ththe nextxtcclolosising priicece..

As we understand the chaararacteristiic cthat all stock markets, including IDX, have in common is the uncerrtainty, whicch is related with their short and long-term future state. This featuree is unddesirable for the investor but it is also unavoidable whenever the stock mmararket is selected as the investment tool. The

1.1. Problem Identification

How to forecast the stock price index? The stock market index direction prediction is regarded as one of the crucial issues in recent financial analysis studies (Wang & Choi 2013). Many techniques are employed to predict stock prices in the stock markets but the past results are being questioned. Generally, there are three schools of thought in terms of the ability to profit from the equity market. The first school believes that no investor can achieve above average trading advantages based on the historical and present information. The major theories include the Random Walk Hypothesis and the Efficient Market Hypothesis (Peters 1991). The Random Walk Hypothesis states that prices on the stock market wander in a purely random and unpredictable way. Each price change occurs without any influence by past prices. The Efficient Market Hypothesis states that the markets fully reflect all of the freely available information and prices are adjusted fully and immediately once new information becomes available. If this is true then there should not be any benefit for prediction, because the market will react and compensate for any action made from these available information. The second school’s view is the so-called fundamental analysis. It looks in depth at the financial conditions and operating results of a specific company and the underlying behavior of its common stock. The value of a stock is established by analysing the fundamental information associated with the company such as accounting, competition, and management. The fundamental factors are overshadowed by the speculators trading.

prediction is regarded as onone of the crucicialal issues in recent financial analysis studies (Wang & & CChoi 2013). Many techniques are ememployed to predict stock prices in n the stock markkeets s bubut t ththee papast results are bbeing questioned. Geennerally, there aaree thrh ee schools of thoughght inin ttererms of the abbilility to profit from thehe eeququiity maarkrkeet. The first school believeess that noo ininvevestsor cannaachieve abbovovee averraage trading advantages based on the hihistsorici alal aandnd preeses nt infformatation. The major theories include the Random Walk HyHypothhesesisis and thhe Ef

E ficicient Market Hypothesis (Peters 1991). The Random Waalkl HHypypototheh sis stattes that prices on the stock market wander in a purely rrandom aandnd unnpredictable way. Each price change occurs without any influennce byy passt pr

pricices. The Efficiienentt MaMarkrketet Hypothesis ststatatesestthahatt ththe markets fullllyy reflect t aalll of the freely available informrmattioion and prices are adjusted fully y anandd im

immem diately once new information becomes available. If this is truuee ththenen tthehere sh

s ouldld nott bbe anany y bebenefit foforr prprededicictitioon, becaaususe e ththe markket will ret reacactt and coompmpensatetefforor any action maaded from ththese available ininfoformation.n.TThe second school’s view is the so-calleed fundameental analysis. It looks in depth at the financial conditions and opeerating resesults of a specific company and the underlying behavior of its commmon sstock. The value of a stock is established by analysing the fundamental inforo mation associated with the company such

Technical analysis assumes that the stock market moves in trends and these trends can be captured and used for forecasting. Technical analysis belongs to the third school of thought. It attempts to use past stock price and volume information to predict future price movements The technical analyst believes that there are recurring patterns in the market behavior that are predictable. In fact, there is not any research proved the existing of such patterns due to each stock market has different characteristics, depending on the economies they are related to, and varying from time to time. Most of the techniques used by technical analysts have not been shown to be statistically valid and many lack a rational explanation for their use. However, technical analysis has its value on forecasting.

Artificial Neural Networks are regarded by many as one of the more suitable techniques for stock market forecasting (Yao & Tan 2001). It has been demonstrated to be an effective technique for capturing dynamic non-linear relationships in stock markets, while technical analysis techniques unable to do so.

1.2. Objective of the Research

The core objective of this research is to forecast the direction of movement in the daily JKSE or IDX using artificial neural network, also to compare a financial performance model and statistical model of ANN on its precision of stock price prediction. At the same times, its empirical result will be comparing with some recently researcher’s works in this market that used ANN models. Also, this research will seek to prove against a validity of the the third school of thoughghtt. IIt attemptst ttoo use past stock price and volume information to pprredict future price movements The e tetechnical analyst believes that therere are recurring patteernns sininthethemmarkeket behavior that tara e predictable. In factct, there is notaanynyrrese earch proved the exististining g ofof such patternsns due to each stock mamarkrketet has ddififfeferent characteristics, depeendnding onon tthehe economimies they are e rerelalatted toto, and varying from time to time. Most of ththe techchniniququeses used dby technicah al analysts have not been shown to be statistically vallidid andd mmany lackck a

a ratitional explanation for their use. However, technical analysiiss has s ititss vav lue on fforecasting.

Arrtificial Neural Networks are regarded by many as one of the mmore ssuuitablle techniquess fforor sstotockck mmararket t fof recasttining g (Y(Yaoao && TTanan 22001). It has bebeenen demonstrated to be an effective tetechnique for capturing dynamic nonn-lilineneaar re

relal tiionnshshipipss inin sstotockck mmararkekets, while techchninicacall ananalalysysisis tectechnhniiquess ununabablee to do

do sso.

1.2. Objective of the Research

The core objective of this ressearch is to o forecast the direction of movement in the daily JKSE or IDX using g artificcial neural network, also to compare a financial performance model and ststatistical model of ANN on its precision of

Efficient Market Hypothesis and the Random Walk Hypothesis for short-term trading advantages in this stock market, which is considered as one of the most important emerging markets in Asia.

1.3. Contribution of the Research

Recently, a number of researchers have explored artificial intelligence techniques such as ANNs to solve financial problems significantly increased, but most has targeted the United States market (Suchira Chaigusin, 2011). There have been limited attempts to research stock markets of developing economies such as Indonesia. At the beginning of this research, the author find that there are some previous research using intelligent approach in this market, but there are not many existing research using artificial neural network technique, specifically, to predict the index movements of the JKSE.

The major contributions of this study are to demonstrate and verify the predictability of stock price index direction using the financial and statistical performances of ANN model. It also benefit to other researchers/students who are interested in studying stock market price movement with ANN model.

This study is one step along the path towards applying ANN to the IDX in order to clarify and predict stock performances. Enhancing the use of ANN in financial areas and contributing incrementally to the growing knowledge base of this financial forecasting field.

most important emerging gmamarkets in Asik iaa.

1.3. Contributionn of the Researchc

Rececently, a nuumbmbeer of reeseseaarchers h s hahaveve eexpxplolorred artificialal intelligence t

techniququeses suuch as ANNNNs to solve financialal pproroblems sisigngnifificicantly inincreased, buut momosts hass targeted the United States market (Sucht chira ChChaiaigugusin, 22010 1). Th

Theere hahave been limited attempts to research stock markekets oof f dedevvelopinng ec

e onoomies such as Indonesia. At the beginning of this research, tht e auauththoror findnddddd thatt there are some previous research using intelligent approach inn thissmmarkeett, buutt there are not many existing research using artificial neurral netwworkk te

techchnique, specifically,y, to ppredict the index movements of the JKSEE..

The major contributions of thihis s ststudy are to demonstrate and verifify y ththe pr

p ededicictatabilityy of stock prp ice index direction using g the financiaiall anandd ststatatisstitical pe

performances ooff ANANN model. IIt t alalsoso bebenefit to oththerer rrese earchers/stutudedentnts who are ininteterereststeded in studying stockk market t price movement wwitithh ANANNN model.

This study is one step alongg the path ttowards applying ANN to the IDX in order to clarify and predict stoockc perfoormances. Enhancing the use of ANN in financial areas and contributing iincn rrementally to the growing knowledge base

1.4. Scope of the Research

As there are many ANN research techniques were used to predict stock price movement index mentioned in 1.1; some of recently research are using international market indicators, technical & fundamental indicators as inputs which it’s called “hybrid”. But some try to combined a lot of indicators as input variable. However, the research finding indicated that many indicators/ or higher number of input variables is not mean such forecasting technique (model) will give a result more accuracy.

In this research, we will attempt to forecast daily stock price movement at JKSE, using financial performance and statistical performance evaluations of ANN models. The data collection with total period of 9 years and 5 months, starting from January 3, 2005 to May 28, 2014. The reason we start from 2005 because the index prices at the beginning 2005 reached RP1.000 and has dramatically increased until the end of 2007 and then in the direction of decrease. At the beginning of 2009 index prices had in the direction of increased again. So, we would like to see how ANN model perform in the different period and direction. The data be divided into two separate sets, a period of January 3, 2005 to December 30, 2010 is for network training purposes. From January 3, 2005 to May 28, 2014 is for testing the predictive ability of the network. A three-layered feed-forward ANN model will be constructed (see Figure. 2). This ANN model consists of an input layer, a hidden layer and an output layer, each of which is connected to the other. Inputs for the network are twelve technical indicators which are represented by twelve neurons in the input layer (see table 2).

movement index mentioneded iinn 11.1;1; ssomo e of recently research are using international markrkeet indicators, technical & fundadamem ntal indicators as inputs which it’ss called “hybrid”. But some try to combined aa llot of indicators as inpuutt variable. HoHowew vever, thee rese earch h fifinddinngg inindidicacated that manany indicators/ or higheer r nunumbm er of ininpput variables is nott mmeae n such ffororececasting ttece hnique (mmododelel))will ggivive a result more accuracy.

In thhisis research, we will attempt to forecast daily stock pprircemmovoveme ent ata JK

J SEE, using financial performance and statistical performance evalluau titionons off ANNNN models. The data collection with total period of 9 years annd 5 mmonthshs, staarting from January 3, 2005 to May 28, 2014. The reason wee start frfromm 20

2 005 because the index pprices at the beginningg 2005 reachedRP1.000000 and hhasas dramatically increased untild l tthehe endnd oof 2007 and then in the directionon oof de

d crease. At the beginning of 2009 index prices had in the dirireectitionn of incrcreaeasesed d agagaiainn. SSo,o, wwee wowoululd d lilkee ttoo seseee hohow w ANANNN momodedel l peperfrforrm m inn the di

diffffererenentt peperiodod aand directionon. The daatata be dividedd ininto ttwowo ssepepararatate sets, a period of January 3, 2005 tto Decembber 30, 2010 is for network training purposes. From January 3, 20005 to Maayy 28, 2014 is for testing the predictive ability of the network. A threee-layeyered feed-forward ANN model will be constructed (see Figure. 2). This AANN model consists of an input layer, a

The forecasting ability of the ANN model is accessed by using back-propagation neural network of errors such as MAE, RMSE, MAPE, and R2, to improve their prediction performances two comprehensive parameter setting experiments for both technical indicators and the levels of the index in the market are performed. The logistic sigmoid transfer function will be used in neural network; this function converts an input value to an output ranging from 0 to 1 (see 2.3). If the connection weight is negative or (value < 0) then tomorrow close price value < than today’s price (loss). If the value is positive (value > 0.5) then tomorrow close price value > than today’s price (profit). As we want to see the direction of movement decrease or increase so the output will be categorized as 0 and 1. Therefore, if the forecasting value smaller than 0.5 it will be categorized as decreased direction.

Published stock data will be collected from Yahoo.Finance especially daily closing price, daily high price, and daily low price of total price index.

1.5. Organization of the Thesis

This thesis is composed of six Chapters. The remaining portion of the thesis is broken up into the following Chapters: Chapter 2 describes Artificial Neural Network and related literature. Chapter 3 describes the research methodology which includes ANN modeling, research data and technical input variables. In Chapter 4 descriptive statistics are used simply to describe the sample. Chapter 5 the empirical results are summarized and discussed. Chapter 6 brief conclusion with some suggestions for the future work on the field of stock market prediction

improve their prediction ppererfformances twtwo o comprehensive parameter setting experiments for r bboth technical indicators and the lelevels of the index in the market aarere performed. The lol gigststicc ssigigmomoidid transfer functitiono will be used in neurural network; ththisis ffunu ction converts an ininpuput t vavalue to an ouutptput ranging from 00ttoo 11(s(see 2.33)). If the connection weighthtiissnegatiiveveoor r (value << 0) then tomomorrrroow clolose price value < than today’s price (loss). IfIf the valalueue iis sposisitive (

(value >l > 0.5) then tomorrow close price value > than todayy’s prricice e (profit)t). As

A wwe want to see the direction of movement decrease or inncreaasese ssoo the outtput will be categorized as 0 and 1. Therefore, if the foreccasting valuluee smmaller than 0.5 it will be categorized as decreased direction.

Pubblished stocockk ddata will bebe collectedd ffrom Yahoh o.oFiFinan nce espep cially ddaiailyly closing price, daily high price, andndddaily low price of total price index.

1.

1.5.5. OrOrgaganinizazatitiononooff ththee ThThesesisis Th

Thisis thesis isisccomompoposesedd ofofssix ChChapapteters. TTheherrememaininingng pporo tion ooff ththee ththesis is broken up into the following CChapterrs:s Chapter 2 describes AArtificial Neural Network and related literaturre. Chapter 3 describes the research methodology which includes ANN modelingg, researcch data and technical input variables. In Chapter 4 descriptive statistics arre used simply to describe the sample.