How To Predict Stock Price From A Model Of Price

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1 0 Ieraioal Coferece o E-busiess, Maageme ad Ecoomics IPEDR Vol.5 (0 (0 IACSIT Press, Sigapore Sock price predicio usig a fusio model of wavele, fuzzy logic ad Nassim Homayoui + ad Ali Amiri Compuer egieerig deparme, Islamic Azad uiversiy, Zaja brach, Zaja, Ira Compuer egieerig group, egieerig deparme, uiversiy of Zaja, Zaja, Ira Absrac. Sock price predicio is vial for makig iformed ivesme decisios ad is receivig icreasig aeio especially because of is pracical applicaios. Predicig he sock marke is very difficul sice i depeds o several ukow facors. The radiioal predicio models are o able o achieve a saisfyig predicio effec i he problem of a o-liear sysem ad o saioary fiacial sigal. O he oher had daa preparaio is a impora sep for complex daa aalysis ad i has a huge impac o he success of predicio. I his paper we propose a fusio model of forecasig by combiig wavele as a daa preparaio ool, fuzzy logic ad eural ework. We have used Tehra sock marke prices as a sample daase o compare simulaio error of sock marke reurs bewee he proposed models. The experimeal resuls show ha his fusio model achieves beer forecasig accuracy ha eiher of he models used separaely. Keywords:, ANFIS, Wavele, Sock prices, Predicio.. Iroducio I rece years, wih he iroducio of olie radig, he sock marke has become oe of he aveues where eve small ivesors ca ear good profis [3]. I would herefore be quie appealig if we ca predic he marke behavior accuraely, so ha ivesors ca decide whe ad where o ives heir moey. Predicio of sock price reur is a highly complicaed ad very difficul ask because here are oo may facors such as poliical eves, ecoomic codiios, raders expecaio ad oher eviromeal facors ha may ifluece sock prices. I addiio, sock price series are geerally quie oisy, dyamic, oliear, complicaed, oparameric, ad chaoic by aure. Preparig daa is a impora ad criical sep i eural ework modelig for complex daa aalysis ad i has a immese impac o he success of a wide variey of complex daa aalysis, such as daa miig ad kowledge discovery. The mai reaso is ha he qualiy of he ipu daa io eural ework models may srogly ifluece he resuls of he daa aalysis [6]. Fuzzy sysems ad eural eworks are cosidered as wo mos widely used echiques i iellige sysems. Auomaic corol, paer recogiio, huma-machie ieracio, exper sysems, modelig, medical diagosis, ecoomics, ec. are some of hese sysems' applicaio areas. Obviously, each echique has is ow advaages ad drawbacks. Fuzzy sysems have he abiliy o represe comprehesive liguisic kowledge ad perform reasoig hrough fuzzy rules. However, fuzzy sysems do o provide a mechaism o ue hose rules. O he oher had, NN are adapive sysems ha ca be raied ad ued from a se of ipu-oupu daa se. Neverheless, i is very difficul o udersad ad represe he obaied kowledge. The remaider of he sudy is orgaized as follows: I Secio, we review he lieraure. I secio 3, a daa preparaio scheme usig wavele is proposed ad he srucure of he fusio models is discussed. I + Tel: address: assim00@yahoo.com 77

2 secio 4, o verify he effecs of daa preparaio o daa aalysis, ad o compare he proposed models, a example daase is used. Fially, he paper eds wih cocludig remarks ad fuure direcios i Secio 5.. Lieraure Review Ademola Olayemi (007 meioed ha forecas performace improves whe pre-processed daa is used. He has used a geeral pre-processig mehod, based o muli scale wavele decomposiio o provide a local represeaio of ime series daa prior o he applicaio of fuzzy models [5]. Jovia Neoraie (007 iroduces a iellige decisio-makig model which is based o he applicaio of arificial eural eworks ( ad swarm ielligece echologies. The proposed model is used o geerae oe-sep forward ivesme decisios for sock markes [4]. Chog Ta (009 proposed a exchage rae forecasig model based o eural ework, saioary wavele rasform ad saisical ime series aalysis echiques. His experimeal resuls demosrae ha he proposed mehod subsaially ouperforms classical approaches [3]. AK Dhamija (00 compares he predicive accuracy of eural eworks ad codiioal models like ARCH, GARCH, GARCH-M, TGARCH, EGARCH ad IGARCH, for forecasig he exchage rae series. His resuls show ha Neural eworks' performace is beer ha ha of codiioal models i forecasig exchage rae []. Ad fially K.S.Vaisla (00 used Neural Neworks ad Saisical echiques o forecas he sock marke prices ad he compared he resuls of hese wo models. All hese resuls show ha Neural Neworks, whe raied wih sufficie daa ad proper ipus, ca predic he sock marke prices very well []. 3. Mehodology The wo models preseed i his paper are caegorized as fusio models. The firs model cosiss of ad wavele ad he secod model cosiss of ANFIS ad wavele wih ecessary modificaios. Fig. shows he srucure of models. Firs we ormalize he daa ad he we pass i o fusio models. A las he effec of daa preparaio usig wavele ad he accuracy of forecasig models are compared. Raw daa Wavele deoisig Daa ormalizaio ANFIS predicio predicio Compare he predicio resuls Fig.: srucure of he models. 3.. Wavele Wavele aalysis is used o process iformaio effecively a differe scales. I is very useful for feaure deecio from complex ad chaoic ime series. I paricular, he specific local properies of waveles ca be useful i describig he sigals wih discoiuous or fracal srucures i he fiacial marke. I also allows he removal of oise depede high frequecies, while coservig he sigal bearig high frequecy erms. However, oe of he mos criical issues i he applicaio of he wavele aalysis is o choose he correc wavele hresholdig parameers. I his sudy, we have used oe dimesioal discree db3 wavele. Also we choosed level 3 because i showed beer resuls ha he oher levels. 3.. Neural ework The foudaio of he arificial eural eworks paradigm was laid i he 950s. Sice he, s have eared sigifica aeio because of he developme of more powerful hardware ad eural algorihms [6]. Arificial Neural Nework is a arificial represeaio of he huma brai ha ries o simulae is learig process. Neural ework modelig for complex daa aalysis has hree mai processes: problem ideificaio, eural ework modelig ad daa aalysis. Fig. shows he eural ework model. 78

3 Fig. : Neural ework model. I his sudy we have illusraed a ework wih oe hidde layer cosisig of four euros. I also has oe oupu ad four ipu odes. To rai he ework we have used feed forward back propagaio algorihm. The oupu layer fucio is ake Sigmoid for he forecasig ANFIS Adapive euro-fuzzy iferece sysems (ANFIS represe a eural ework approach o he desig of fuzzy iferece sysems. ANFIS is a fuzzy iferece sysem ha ca be raied o model he collecio of ipu-oupu daa. There are wo approaches used by ANFIS: Arificial eural ework ad fuzzy modelig. I ANFIS, fuzzy logic is used o deermie decisio surfaces raher ha he uceraiy associaed wih paricular liguisic erms [7]. For educaioal purposes, oe ca imagie a fuzzy iferece sysem wih wo ipus x ad y ad oe oupu z. Figure.3 shows he equivale ANFIS archiecure. The firs layer implemes a fuzzificaio, he secod layer execues he T-orm of he aecede par of he fuzzy rules, he hird layer ormalizes he membership fucios, he fourh layer calculaes he coseque parameers, ad fially he las layer compues he overall oupu as he summaio of all icomig sigals. Fig. 3: The equivale ANFIS srucure. We have used bell membership fucios o each of he four ipus. The geeraed FIS srucure coais 6 fuzzy rules. 4. Experimes ad resuls We have used daily sock price of ISC from Jauary 005 o Jauary 00 as a sample daase o compare simulaio error of sock marke reurs bewee he proposed models. The daa is obaied from semc websie. I his sudy he four aribues: ope, high, low ad close price from he daily sock marke are used o form he observaio vecor. The forecas variable is he ex day's closig price. Fig.4(a shows he oupu of ANFIS wihou usig wavele ad Fig.4(b shows he oupu whe we use he wavele as a daa preparaio ool. Fig.5 shows he oupu of wih ad wihou usig wavele. Oupu ad arge daa are compared i hese figures. Iformaio Services Corporaio 79

4 (a Fig.4: ANFIS oupus. (b (a Fig.5: oupus. (b We employ five measures o evaluae he performace of differe forecasig models. Here hey are: Mea Absolue Error (MAE = y = Mea Absolue Perceage Error (MAPE = Mea Squared Error (MSE = ( y = Roo Mea Squared Error (RMSE = y y = ( y = Normalized Mea Square Error (NMSE = ( y = ( y y = Where y ad ŷ are he acual ad prediced values, y is he mea value of y. The smaller values of error, he closer are he prediced values o he acual values. Fig.6 ad able. show he resuls of he experime. Table. Compariso of models. ( ( (3 (4 (5 MAE MSE MAPE RMSE NMSE Wavele ANFIS ANFIS + Wavele

5 MAE MSE MAPE RMSE NMSE +Wavele ANFIS ANFIS+Wavele Fig. 6: Compariso of models diagram. The above compariso clearly show ha he hybrid model of ANFIS ad wavele i MSE, RMSE ad NMSE measures ouperforms he oher model which uses i predicio phase. I MAE ad MAPE measures usig ANFIS aloe, ouperforms all ohers. 5. Coclusio I his paper we proposed a fusio model of forecasig by combiig wavele, fuzzy logic ad eural ework. We used Tehra sock marke prices as a sample daase o compare simulaio error bewee he proposed models. The forecasig abiliy of models accessed o he basis of MSE, MAE, RMSE, MAPE ad NMSE. The experimeal resuls show ha his fusio model achieves beer forecasig accuracy ha eiher of he models used separaely. The logical ex sep for he research is o improve he accuracy of predicio usig NNs, hrough beer raiig mehods, beer ipu variable selecio ad beer daa preparaio ools for chaoic ime series. 6. Refereces: [] AK Dhamija - Fiacial ime series forecasig: compariso of ad ARCH model Ieraioal research joural of fiace (00 [] KS vaisla - sock marke forecasig usig arificial eural ework ad saisical echique: a compariso repor - ieraioal joural of compuer ad ework securiy- (00 [3] Chog.Ta Fiacial ime series forecasig usig improved wavele Maser hesis repor (009 [4] Jovia eoraie - A paricle swarm opimizaio approach i he cosrucio of decisio makig model - iformaio echology ad corol (007 [5] Ademola Olayemi Fuzzy wavele mehod for ime series aalysis uiversiy of Surrey (007 [6] Lea Yu - A Iegraed Daa Preparaio Scheme for Neural Nework Daa Aalysis - IEEE Trasacios o kowledge ad daa egieerig (006 [7] Meysam Alizadeh Desig of experimes i ANFIS modelig for sock predicio - Ieraioal Joural of Idusrial Egieerig Compuaios (0 [8] Chi Yua Yeh - A SVR approach for sock marke price forecasig Exper sysems wih applicaios (0 [9] Dase R.K Applicaio of for sock marke predicio Ieraioal joural of ielligece (00 [0] K.Sehamarai Kaa Fiacial sock marke forecasig usig daa miig echiques Ieraioal muli coferece of egieers ad compuer scieiss (00 [] George S.Asalakis Surveyig sock marke forecasig echique Exper sysems wih applicaios (009 [] Hsue Cheg Fuzzy ime series for sock idex forecasig Exper sysems wih applicaios (009 8

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