Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators

Transcription

1 Joural of Iellige Learig Sysems ad Applicaios, 2011, 3, doi: /jilsa Published Olie November 2011 (hp:// 209 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors Qi Qi, Qig-Guo Wag, Shuzhi Sam Ge, Gaesh Ramakrisha Deparme of Elecrical ad Compuer Egieerig, Naioal Uiversiy of Sigapore, Sigapore Ciy, Sigapore. Received July 19 h, 2011; revised Sepember 19 h, 2011; acceped Ocober 8 h, ABSTRACT While a large umber of sudies have bee repored i he lieraure wih referece o he use of Regressio model ad Arificial Neural Nework (ANN) models i predicig sock prices i weser couries, he Chiese sock marke is much less sudied. Noe ha he laer is growig rapidly, will overake USA oe i years ime ad hus becomes a very impora place for ivesors worldwide. I his paper, a aemp is made a predicig he Shaghai Composie Idex reurs ad price volailiy, o a daily ad weekly basis. I he paper, wo differe ypes of predicio models, amely he Regressio ad Neural Nework models are used for he predicio ask ad muliple echical idicaors are icluded i he models as ipus. The performaces of he wo models are compared ad evaluaed i erms of direcioal accuracy. Their performaces are also rigorously compared i erms of ecoomic crieria like aualized reur rae (ARR) from simulaed radig. I his paper, boh radig wih ad wihou shor sellig has bee cosidered, ad he resuls show i mos cases, radig wih shor sellig leads o higher profis. Also, boh he cases wih ad wihou commissio coss are discussed o show he effecs of commissio coss whe he radig sysems are i acual use. Keywords: Regressio Model, Arificial Neural Nework Model, Chiese Sock Marke, Techical Idicaors, Volailiy 1. Iroducio From he begiig of ime i has bee ma s commo goal o make his life easier. The prevailig oio i sociey is ha wealh brigs comfor ad luxury, so i is o surprisig ha here has bee so much work doe o ways o predic he markes. Various echical, fudameal, ad saisical idicaors have bee proposed ad used wih varyig resuls. However, o oe echique or combiaio of echiques has bee successful eough o cosisely bea he marke. As a resul, here is a huge moivaio o develop ew forecasig echiques ha ca uravel he marke s myseries ad obai greaer profis. The sock marke is kow as he cradle of capialism. I is a place where compaies come o raise heir share capial ad ivesors go o ives heir surplus fuds. Vas amous of capial are ivesed ad raded i everyday all over he world. The predicio of he sock marke movemes, however, poses a challege o academicias ad praciioers. The reaso is ha sock marke movemes are characerized as beig ucerai ad complex as i ca be affeced by virually ay ecoomic, social or poliical developme ha has a bearig o he ecoomy. This uceraiy ad complexiy is udesirable for ay rader who is aempig o make profis from he sock marke. Therefore, here is a eed o reduce his uceraiy by makig accurae predicios. Iiially, sock marke research ecapsulaed wo elemeal radig echiques amely he Techical ad Fudameal approaches [1]. I Techical aalysis, i is believed ha marke imig is keypoi. I ivolves he sudy of hisorical daa of he sock marke o predic reds i price ad volume. I oher words, here is heavy reliace o hisorical daa i order o predic fuure oucomes. Fudameal aalysis, o he oher had ivolves makig esimaes o he irisic value of a sock. This echique uses iformaio such as earigs, raios, ad maageme effeciveess o predic fuure oucomes. As he level of ivesig ad radig grew, here bega a pursui for beer ools ad mehods ha could o oly icrease gais bu also miimize he risks uderake by he ivesor. Tools ha used modelig echiques o discover paers wihi he hisorical daa of he sock marke were pu o es, wih a aemp o predic ad bee-

2 210 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors fi from he marke s direcio. Oe such example is he Liear Time Series Models, where uivariae ad mulivariae regressio models [2] were used o ideify paers i he hisorical daa of he sock marke. For oliear paers, Machie Learig Models [3], i paricular eural eworks, were commoly used. For example, oe sees ha: I [4], he auhors used a mea reverig characerisic o model ad esimae he sock markes. The auhors saed ha he radom walk which is used o describe he sock markes may o be correc whe he process of sock markes diverge over ime. The mea reverig characerisic is a good way o model ad esimae he sock markes. The auhors used wo mehods o esimae he parameers, which are Leas Square Esimaio ad Maximum Likelihood Esimaio. I his paper, he auhors focused o he mohly daa of Dow Joes Idusrial Average ad he Sigapore Srais Times Idex ad go some ieresig coclusio. I [5], he auhors prediced he mid-erm price red for Taiwa sock marke. The auhors firsly exraced he feaures from ARIMA aalyses, he he auhors used he feaures which are produced i he firs sep o rai a recurre eural ework. The Taiwa sock marke series is regarded by he auhors as a oliear ARIMA (1,2,1). The coclusio of his paper is ha he predicio sysem ca predic he Taiwa sock marke red of up o 6 weeks based o four years weekly daa wih a accepable accuracy. I [6], he auhors focused he research work o Shaghai sock marke for Chiese sock marke is oe of fas growig sock markes i he world. The auhors used wo ypes of models which are he model of sochasic SARIMA ad he model of backpropagaio ework. The auhor used he acual daa of Shaghai Composie Idex o do he predicio ad foud ha SARIMA model is more opimisic. I [7], he auhors ook advaage of he oliear dyamical heory o use he mulivariae oliear predicio mehod. The predicio sysem is based o he recosrucio of mulidimesioal phase space. The auhors se he model usig mulivariae oliear predicio mehod ad go he experime resuls usig he daa of Shezhe Idex. The auhors compared he resuls obaied usig mulivariae oliear predicio mehod wih he resuls obaied usig uvariae oliear predicio mehod ad foud ha he performace of mulivariae oliear predicio mehod is beer ha he performace of uvariae oliear predicio mehod. I [8], he auhor saed ha he sock marke is a very complicaed oliear sysem, he arificial eural ework also has oliear characerisic. I is proper o use arificial eural ework o do he predicio of sock marke. The auhors used he arificial eural ework o imiae he radig process of sock marke. Because he coverge speed of backpropagaio algorihm is low, he auhors ehaced he coverge speed of backpropagaio algorihm by proposig he rae of deviaio. The auhors used he daa of boh Shaghai ad Shezhe o do he predicio. I [9], he auhors explored a ew mehod o esimae he sysemaic risk (which is called as bea) i Chia sock marke. A echique is ivolved i his ew mehod, which is maximal overlap discree wavele rasform (MODWT). The echique will o lose ay iformaio whe i is ivesigaig he behavior of bea a differe ime frames. The experimeal resuls showed ha Chia sock marke is quie differe from oher sock markes. The auhors drew a coclusio ha he differece bewee Chia sock marke ad oher sock markes is due o he characer ad behavior fiace. I [10], he auhors aalyzed he volailiy of a sock i Chia o is reurs series usig he models of GARCH family. They foud ha he series of sock reurs is saioary, ad i has a sigifica ARCH effec, a volailiy cluser exiss i Chia sock marke. The auhors also foud ha a reur of egaive shock produces more volailiy ha he posiive oe of equal magiude. They fially drew a coclusio ha here is a leverage effec i sock reurs volailiy. I [11], he auhors used he daily daa of Shaghai socks o do he predicio based o he family GARCH models. The paper used ME, MAE ad R- MSE for error measureme. From he resuls, he auhors foud ha i he raiig period, EGAR- CH-M model ca geerae bes performace, while i esig period, simple GARCH model or asymmeric model ca produce bes performace. I geeral, mos of sock marke sudies i he lieraure have bee focused o developed markes while emergig markes are much less sudied. Noe ha he laer is growig rapidly, ad i paricular, Chia marke will overake USA oe i years ime ad i has becomes a very impora place for ivesors worldwide. I is hus imely o sudy his marke's performace ad efficiecy based o rece daa. This paper aemps o predic he Shaghai Composie Idex reur ad volailiy o a daily ad weekly basis wih use of muliple echical

3 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors 211 idicaors. Specifically, he prese work coribues o he lieraure i he followig ways: 1) A aemp is made o udersad he efficacy of a emergig marke such as Chia. Today, Chia is oe of he fases growig emergig ecoomies i he world. No oly is here a sigifica growh i he demad for ivesme fuds bu he growh i capial markes is also expeced o play a icreasigly impora role i he process. A his rasiioal sage, i is ecessary o assess he level of efficiecy of he Chiese Sock Marke i order o esablish is loger erm role i he process of ecoomic developme. However as sudies o Chiese Sock Markes are very few ad also daed ad mosly icoclusive, he objecive of his sudy i his paper is o es wheher predicabiliy of reur raes ad price volailiy is possible. 2) A aemp is made o predic sock marke price volailiy. Volailiy is a impora idicaor for ivesors. Resuls from his sudy do show ha eural ework models have heir meris ad perform beer ha regressio models. 3) Muliple echical idicaors are used i modelig. We also use differe combiaios of differe echical idicaors o do he predicio o see he performace. Some combiaios improve he performace of he predicio. The res of he paper is orgaized as follows. Secio 2 gives a overview of he sock marke predicio mehods. Secio 3 preses he mehodology ad shows he resuls for he predicabiliy of Shaghai Composie Idex reur. Secio 4 preses he mehodology ad shows he resuls for he predicabiliy of Shaghai Composie Idex price volailiy. Fially, Secio 5 gives a coclusio of he work ha has bee doe, as well as possible areas of improvemes i fuure work. 2. Sock Marke Predicio Mehods I his secio, we will cosider he differe predicio mehods ha are available for predicig sock marke movemes ad reurs. Some of hese mehods ha will be covered i deph i his secio are Techical Aalysis, Liear Time Series Models ad Machie Learig Models Techical Aalysis The idea behid echical aalysis is ha sock prices move i reds dicaed by he cosaly chagig aiudes of ivesors i respose o differe forces. Fuure sock movemes are prediced by usig price, volume ad observig reds ha are domiaig he marke. Techical aalysis ress o he assumpio ha hisory repeas iself ad ha fuure marke direcio ca be deermied by examiig pas prices [1]. The groups of pro- fessioals who subscribe o his mehod are he echical aalyss or he chariss, as hey are more commoly kow. To hem all iformaio abou earigs, divideds ad fuure performace of he compay is already refleced i he sock s price hisory. Therefore he hisorical price char is all a charis eeds o make predicios of fuure sock price movemes. This mehod of predicig he marke is highly criicized because i is highly subjecive. Two echical aalyss sudyig he same char may ierpre hem differely, hereby arrivig a compleely differe radig sraegies. Also a charis may oly occasioally be successful if reds perpeuae. Techical aalysis is also cosidered o be coroversial as i coradics he Efficie Marke Hypohesis. Despie such criicism ad coroversy, he mehod of echical aalysis is used by approximaely 90% of he major sock raders. I his paper, several echical idicaors are used. I will show he deails of he echical idicaors blow: 1) Movig Average: This idicaor reurs he movig average of a field over a give period of ime. This is doe primarily o avoid oise i he daily price movemes. The formula of MA used i his chaper is showed below: MA mea (las close prices) (1) The is he parameer. We se as 10 ad 25 i his paper. 2) Oscillaor: This fucio compares a securiy s closig price o is price rage over a give ime period. The formula of SO used i his chaper is showed below: close price L % K 100 (2) H L % D 3 period movig average of % K (3) where H ad L are respecively he highes ad he lowes price over he las periods. The is he parameer. We se as 10. 3) Volailiy: Volailiy ca eiher be measured by usig he sadard deviaio or variace bewee reurs from he sock or marke idex. Commoly, he higher he volailiy, he riskier he sock or marke. The formula of volailiy used i his chaper is showed below: Volailiy = sd las close prices (4) The is he parameer. We se as 10. Beside he echical idicaors above, we also used some simple echical idicaors: reur, acual price chage, volume ad volume differece Liear Time Series Models Liear ime series models are ofe used o predic fuure values of he ime series by deecig liear relaioships

4 212 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors bewee he hisorical daa of he sock ad he ime series uder cosideraio. [2] Depedig o he umber of differe variables used as facors of he ime series, wo differe ypes of liear ime series models are used. For he case where oly oe facor is used o predic he ime series, uivariae regressio is employed. If more variables are used o predic he ime series, he he model of mulivariae regressio is used. The regressio mehod works by havig a se of idepede variables, whole liear combiaio gives he prediced value of he ime series uder cosideraio. The prediced value of he ime series is hus called he depeda variable. The model associaed wih such a regressio mehod is give by he Equaio (5) below: y m a x (5) 1 where y is he depede variable of he ime series a ime, a is he regressio coefficie ad x, is he idepede variable(s). For uivariae regressio, m 1, whereas for mulivariae regressio, m 1. I his paper, liear regressio model will be used. Regressio models are saisical models ha are used o predic oe variable from oe or more oher variables. Iferece based o such models is called Regressio aalysis, which is he echique for modelig ad aalyzig several variables, whe he focus is o he relaioship bewee a depede variable ad oe or more idepede variables. More specifically, he regressio model helps i udersadig how he ypical value of he depede variable chages whe ay oe of he idepede variables is varied. Give a daa se yi, xi 1,..., xip of saisical uis, i1 a liear regressio model assumes ha he relaioship bewee he depede variable yi ad he p-vecor of regressors x i is approximaely liear. This approximae relaioship is modeled hrough a disurbace erm i a uobserved radom variable ha adds oise o he liear relaioship bewee he depede variable ad regressors. The model is described by he fucio give below: y x x x i (6) ' i 1 i1 p ip i i i 1,..., Here yi is he forecased reur or volailiy ha is based o p idepede variables, x i1 o x ip ad 1 o p are he coefficies of he liear regressio model. These equaios are ofe sacked ogeher ad wrie i vecor form as: where, y X (7) y1 y2 y, y ' x 1 x x ' x2 x x X = ' x x1 x 11 1p 21 2 p p, ,. (8) p p The sudy usig he liear regressio model is achieved usig he regress fucio i MATLAB, which akes i he ipus o he model ad he desired oupu from he model ad reurs he coefficies of he liear regressio model. The coefficies of he liear regressio model are obaied by he leas mea squares mehod, which miimizes a error fucio which is he square of he error of each prediced value Machie Learig Models Machie learig models [3] are a class of models which ca sudy he uderlyig relaioships bewee he idepede variables ad he depede variables of he ime series by beig raied o a sample se of daa which should ideally be represeaive of he acual evirome. The mos popular machie learig model used for sock marke predicio is ha of eural eworks (NNs), hus my research work will be focusig o he use of NNs i predicig he Shaghai Composie Idex reurs. NN is a powerful daa modelig ool ha is able o capure ad map a ipu (idepede variable) se o a correspodig oupu (depede variable) se. The moivaio for he developme of he NN echology semmed from he desire o develop a arificial sysem ha could perform iellige asks similar o hose performed by he huma brai. A NN ca resemble he huma brai i wo ways: 1) A NN acquires kowledge hrough learig. 2) A NN s kowledge is sored wihi ier-euro coecio sreghs kow as syapic weighs. The NN archiecure ca be used o represe boh liear ad o-liear relaioships. For daa ha coais oliear characerisics, radiioal liear models are simply iadequae. The mos commo eural ework model is ha of he Muli-Layer Percepro (MLP) ad his sudy o he Chiese sock marke predicio will focus o he MLP. The MLP is also kow as he supervised ework because i requires a desired oupu i order o lear. The goal of his ype of ework is o creae a model ha correcly maps he ipu o he oupu usig hisorical daa so ha he model ca he be used o produce he oupu

5 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors 213 whe he desired oupu is ukow. A graphical represeaio of a MLP wih wo hidde layers is show i Figure 1 below: The MLP is i fac a disribued processig ework, comprisig of umerous euros, wih each euro as he mos basic processig eleme wihi he ework [12]. A euro is a processig ui ha akes i a umber of ipus ad gives a disic oupu for he ipu i receives. The ipus are fed o each euro hrough liks bewee he differe layers. A MLP oly allows liks bewee successive layers of euros. Each lik is characerized by a weigh value, ad i is his weigh value where he memory or kowledge of he problem is sored. The oupu of each euro is deermied joily by he weighed sum of he ipus, as well as he aciveio fucio, f, used i he euro. The mos commoly used acivaio fucios are he hardlimi, liear, sigmoid ad asigmoid acivaio fucios. As depiced i Figure 1, he MLP is made up of a umber of layers of euros. The ipu layer defies he ipus o he MLP. The ipus are he passed o o he firs hidde layer of he MLP. For a MLP, he umber of hidde layers mus be a leas oe. Afer propagaig hrough all he hidde layers, he ipu fially reaches he oupu layer, which he gives he fial oupu of he whole ework for he give se of ipus. A commo oaio o represe he archiecure of he MLP is o use he srig R-S1-S2-S3, where R is he umber of ipus o he MLP, S1 ad S2 idicaes he umber of euros i he firs ad secod hidde layer respecively, ad S3 idicaes he umber of euros i he oupu layer, which is also he umber of oupus i he oupu se of he ework. Afer he archiecure of he MLP has bee decided, he ework will have o be raied before i ca be used i ay applicaio. This procedure of raiig ivolves modifyig he weighs of he liks wihi he MLP so ha he MLP will sore he correc kowledge of he sysem which i is modelig. The raiig procedure for Figure 1. Archiecure of MLP wih wo hidde layers [3]. a MLP ca be doe usig a back-propagaio algorihm o updae he all he weighs of he euros i order o derive a good fi o he raiig daa, bu a he same ime o sacrificig performace o he usee daa. This meas ha a well-raied MLP mus be able o geeralize well from he raiig daa ha is preseed o i. 3. Predicabiliy of Shaghai Composie Idex Reur I his secio, we firsly iroduce he simulaio desig which cosiss of daa collecio, daa pre-processig, hree compariso experimes ad he merics for performace evaluaio, he he simulaio resuls ad discussios are showed Simulaio Desig Daa Collecio We colleced he hisorical daa of Shaghai Composie Idex for boh daily daa ad weekly daa from he year 2000 o he year 2010 from he socksar websie [13] Daa Pre-Processig Because we wa o see wheher he reur is radom or o, we calculae he daily reurs from he daily daa, he weekly reurs from he weekly daa for he Shaghai Composie Idex. The eire se is divided io a hree separae daa ses for differe usage. The firs daa is called he Traiig daa se ad is used for raiig ad adjusig he coefficies or weighs of he sysems. The secod is he Verificaio daa se which is used for verifyig he predicive performace of he raied sysems ad evaluaig he choice of parameers for a good radig sysem. Fially he hird daa se or Tes daa se is used for a acual radig es o deermie he radig performace of he chose radig sysem. We se he raiig daa from 2000 o 2006, he verificaio daa from 2007 o 2008 ad he es daa from 2009 o Predicabiliy Experimes of Shaghai Composie Idex reur The sudy for he predicabiliy of daily ad weekly Shaghai Composie Idex reur is esed usig hree experimes: 1) I Experime I, 10 lags of Shaghai Composie Idex reurs are used for he predicio of he subseque period s reur. 2) I Experime II, he acual Shaghai Composie Idex reurs of up o 10 lags, 10-period movig average of closig Shaghai Composie Idex values, 25-period movig average of he same ad a 10-period oscillaor is used for he predicio of he subseque period s reur. 3) I Experime III, he acual Shaghai Composie Idex reurs of up o 10 lags, 10-period movig average of closig Shaghai Composie Idex values, 25-period

6 214 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors movig average of he same ad a 10-period volailiy idicaor is used for he predicio of he subseque period s reur. I Experimes II ad III, he erm period refers o daily or weekly based o he coex of he experime. I each of he hree experimes, he efficacy of he regressio ad eural ework models i predicig he subseque period s Shaghai Composie Idex reur is evaluaed Merics Used for Performace Evaluaio The performace of all he radig sysems used i his paper will be accessed usig wo merics: The firs meric is he perceage of correc sigs of prediced reurs as compared o he acual reurs. This is ermed as direcioal accuracy i his paper. I has bee argued i lieraure ha for predicio o he sock marke, he sigs of reurs are more impora ha he acual magiude of reurs. Also, i has bee show by Pesara ad Timmerma [2] ha direcioal accuracy measures has a higher correlaio wih reurs compared o usig he mea square error Aual Reur Rae The secod meric is he aual reur rae (ARR) from simulaed radig. The ARR idicaes he aual reurs from radig wih a iiial ivesme of 1 (ARR of 1.1 idicaes a 10% profi). I his paper, boh radig wih ad wihou shor sellig has bee cosidered. Also, boh he cases wih ad wihou commissio coss are discussed o show he effecs of commissio coss whe he radig sysems are i acual use. As meioed earlier, commissio coss play a sigifica role whe he umber of rasacios ges large. I his paper, he commissio cos is assumed o be 0.2% per rade (a sigle rade idicaes eiher a buyig or sellig decisio), which is a raher coservaive amou. I compuig he ARR for radig performace evaluaio, he cumulaive reurs for he whole period (raiig or verificaio) is calculaed firs. Afer which, he ARR is obaied by akig he h roo of he cumulaive reurs, where is he umber of years i he period. I calculaig he cumulaive reurs, wo possibiliies exis depedig wheher a log or shor posiio is held. I he case of a log posiio, he cumulaive reur afer period is calculaed as: f Cumulaive Reurs Cumulaive Reurs 1Acual Reurs (9) For a shor posiio, he cumulaive reur i period is: Cumulaive Reurs Cumulaive Reurs 1Acual Reurs (10) For he radig decisio made i his chaper, he hreshold-based radig rule is used. The hreshold based radig rule is based o boh he magiude ad sigs of predicios made by he sysems. This decisio-makig radig rule is used o make radig decisios via he followig clauses: 1) If he prediced reur rae is posiive ad is magiude greaer ha he hreshold value, he a log (buy) posiio is recommeded. 2) Aleraively, if he prediced reur rae is egaive ad is magiude greaer ha he hreshold, he a shor (sell) posiio is recommeded. 3) If he above codiios fail, 3 scearios are possible whereby he recommedaio is o say away from he marke. If already i a log posiio, wihdraw from marke if he prediced reur rae is egaive. O he oher had, if already i a shor posiio, wihdraw from marke if he reur rae is posiive. Else, he curre posiio is maiaied. The use of his hreshold-based radig rule leads o he eed o vary he hreshold value used i order o fid a appropriae value for he radig sysem which leads o good radig performaces Simulaio Resuls Predicabiliy Experimes of Shaghai Composie Idex Reur For coveiece, i he preseaio of ables, we deoe direcioal accuracy as, aual reur rae as ARR ad commissio fee as CF. For daily daa, we firsly use he regressio model o do he predicio. We show he experime resuls i Tables 1-3: Table 1. Performace of regressio model i experime I (daily). Traiig 54.21% Verificaio 54.81% Table 2. Performace of regressio model i experime II (daily). Traiig 52.90% Verificaio 55.65%

7 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors 215 I experime I, he hreshold for radig is I experime II, he hreshold for radig is I experime III, he hreshold for radig is From he Tables 1-3, we ca see ha he experime I showed he bes performace of regressio model. So we choose he mehod i experime I for es period. We show he resul i Table 4. I experime I, he hreshold for radig is ad he umber of odes is 18. I experime II, he hreshold for radig is ad he umber of odes is 18. I experime III, he hreshold for radig is ad he umber of odes is 12. From he Tables 5-7, we ca see ha he experime II showed he bes performace of NN model. So we choose he mehod ad parameers i experime II for es period. We show he resul i Table 8. Table 3. Performace of regressio model i experime III (daily). Traiig 53.68% Verificaio 57.11% Table 4. Performace of regressio model i experime I (daily). Tesig 56.63% Table 5. Performace of NN model i experime I (daily). Traiig 55.89% Verificaio 55.86% Table 6. Performace of NN model i experime II (daily). Traiig 58.88% Verificaio 55.60% Table 7. Performace of NN model i experime III (daily). Traiig 55.37% Verificaio 55.81% For he weekly daa, we firsly use he regressio model o do he predicio. We show he experime resuls i Tables I experime I, he hreshold for radig is I experime II, he hreshold for radig is I experime III, he hreshold for radig is From he Tables 9-11 above, we ca see ha he experime II showed he bes performace of regressio model. So we choose he mehod i experime III for es period. We show he resul i Table 12 below. Table 8. Performace of NN model i experime II (daily). Tesig 56.02% Table 9. Performace of regressio model i experime I. (weekly) Traiig 62.86% Verificaio 56.67% Table 10. Performace of regressio model i experime II (weekly). Traiig 59.40% Verificaio 66.67% Table 11. Performace of regressio model i experime III (weekly). Traiig 59.06% Verificaio 44.57% Table 12. Performace of regressio model i experime II (weekly). Tesig 55.25%

8 216 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors For he weekly daa, we he use he NN model o do he predicio. We show he experime resuls i Tables I experime I, he hreshold for radig is ad he umber of odes is 14. I experime II, he hreshold for radig is ad he umber of odes is 12. I experime III, he hreshold for radig is ad he umber of odes is 20. From he Table 13 ~ 15, we ca see ha he experime II showed he bes performace of NN model. So we choose he mehod ad parameers i experime II for es period. We show he resul i Table 16. From he resuls showed above, we ca see ha he performace of NN model is beer ha he performace of regressio model. We also ca fid ha for he daily daa, he ARRs of boh regressio model ad NN model are beer ha he ARRs of buy-ad-hold sraegy i esig period (esig period ARR is ). Uforuaely, for he weekly daa, he ARRs of boh regressio model ad NN model are worse ha he ARRs of buyad-hold sraegy. Table 13. Performace of NN model i experime I (weekly). Traiig 66.29% Verificaio 53.33% Table 14. Performace of NN model i experime II (weekly). Traiig 62.39% Verificaio 56.67% Table 15. Performace of NN model i experime III (weekly). Traiig 65.63% Verificaio 52.17% Table 16. Performace of NN model i experime II (weekly). Tesig 62.77% Predicabiliy of Shaghai Composie Idex Price Volailiy I his secio, we firsly iroduce he simulaio desig which cosiss of daa collecio, daa pre-processig, hree compariso experimes ad he merics for performace evaluaio, he he simulaio resuls ad discussios are showed Simulaio Desig Daa Collecio We colleced he hisorical daa of Shaghai Composie Idex for boh daily daa ad weekly daa from he year 2000 o he year 2010 from he socksar websie [13] Daa Pre-Processig Because we wa o see wheher he reur is radom or o, we calculae he daily reurs from he daily daa, he weekly reurs from he weekly daa for he Shaghai Composie Idex. The eire se is divided io a hree separae daa ses for differe usage. The firs daa is called he Traiig daa se ad is used for raiig ad adjusig he coefficies or weighs of he sysems. The secod is he Verificaio daa se which is used for verifyig he predicive performace of he raied sysems ad evaluaig he choice of parameers for a good radig sysem. Fially he hird daa se or Tes daa se is used for a acual radig es o deermie he radig performace of he chose radig sysem. We se he raiig daa from 2000 o 2006, he verificaio daa from 2007 o 2008 ad he es daa from 2009 o Predicabiliy Experimes of Shaghai Composie Idex Price Volailiy The sudy for he predicabiliy of daily ad weekly Shaghai Composie Idex price-chages is esed usig hree experimes: 1) I Experime IV, 10 lags of acual Shaghai Composie Idex closig price values ad 10 lags of periodic price-chages are used for he predicio of he subseque period s price-chage. 2) I Experime V, 10 lags of acual Shaghai Composie Idex radig volume values AND 10 lags of periodic radig volume differeces are used for he predicio of he subseque period s price-chage. 3) I Experime VI, 10 lags of acual Shaghai Composie Idex closig price values, 10 lags of periodic price-chages, 10 lags of radig volume values ad 10 lags of periodic radig volume differeces are used for he predicio of he subseque period s price chage Merics Used for Performace Evaluaio The performace of all he radig sysems used i his paper will be accessed oly usig oe meric:

9 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors 217 This meric is he perceage of correc sigs of prediced reurs as compared o he acual reurs. This is ermed as direcioal accuracy i his chaper. I has bee argued i lieraure ha for predicio o he sock marke, he sigs of reurs are more impora ha he acual magiude of reurs. Also, i has bee show by Pesara ad Timmerma [2] ha direcioal accuracy measures has a higher correlaio wih reurs compared o usig he mea square error Simulaio Resuls Predicabiliy Experimes of Shaghai Composie Idex Price Volailiy For daily daa, we firsly use he regressio model o do he predicio. We show he experime resuls i Tables From he Tables 17-19, we ca see ha he experime VI showed he bes performace of regressio model. So we choose he mehod i experime VI for es period. We show he resul i Table 20. For he daily daa, we he use he NN model o do he predicio. We show he experime resuls i Tables Table 17. Performace of regressio model i experime IV (daily). Traiig 52.96% Verificaio 56.28% Table 18. Performace of regressio model i experime V (daily). Traiig 54.27% Verificaio 54.39% Table 19. Performace of regressio model i experime VI (daily). Traiig 56.84% Verificaio 55.02% Table 20. Performace of regressio model i experime VI (daily). Tesig 57.36% I experime IV, he umber of odes is 16. I experime V, he umber of odes is 10. I experime III, he umber of odes is 10. From he Tables 21-23, we ca see ha he experime VI showed he bes performace of NN model. So we choose he mehod ad parameers i experime VI for es period. We show he resul i Table 24. For he weekly daa, we firsly use he regressio model o do he predicio. We show he experime resuls i Tables Table 21. Performace of NN model i experime IV (daily). Traiig 55.98% Verificaio 54.23% Table 22. Performace of NN model i experime V (daily). Traiig 53.14% Verificaio 53.49% Table 23. Performace of NN model i experime VI (daily). Traiig 57.29% Verificaio 55.60% Table 24. Performace of NN model i experime VI (daily). Tesig 58.18% Table 25. Performace of regressio model i experime IV (weekly). Traiig 59.70% Verificaio 54.39% Table 26. Performace of regressio model i experime V (weekly). Traiig 59.70% Verificaio 54.35% Table 27. Performace of regressio model i experime VI (weekly). Traiig 61.10% Verificaio 56.74%

10 218 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors From he Tables 25-27, we ca see ha he experime VI showed he bes performace of regressio model. So we choose he mehod i experime VI ad he parameers for es period. We show he resul i Table 28. For he weekly daa, we he use he NN model o do he predicio. We show he experime resuls i Tables I experime I, he umber of odes is 12. I experime II, he umber of odes is 20. I experime III, he umber of odes is 16. From he Tables 29-31, we ca see ha he experime VI showed he bes performace of NN model. So we choose he mehod ad parameers i experime VI for es period. We show he resul i Table 32. Similar wih he coclusios of he experime I, II ad III, from he resuls showed above, we ca see ha he performace of NN model is beer ha he performace of regressio model. Table 28. Performace of regressio model i experime VI (weekly). Tesig 59.66% Table 29. Performace of NN model i experime IV (weekly). Traiig 61.19% Verificaio 58.70% Table 30. Performace of NN model i experime V (weekly). Traiig 61.97% Verificaio 56.70% Table 31. Performace of NN model i experime VI (weekly). Traiig 65.79% Verificaio 58.52% Table 32. Performace of NN model i experime VI (weekly). Tesig 59.70% 5. Coclusios ad Fuure Work I his paper, we do he predicio of Shaghai Composie Idex reur ad he predicio of Shaghai Composie Idex volailiy based o regressio model ad NN model usig he daily ad weekly daa of Shaghai Composie Idex. The direcioal accuracy of mos of he experimes is beyod 55%. For he predicio of Shaghai Composie Idex reur, boh radig wih ad wihou shor sellig has bee cosidered, ad he resuls show i mos cases, radig wih shor sellig leads o higher profis. Also, boh he cases wih ad wihou commissio coss are discussed o show he effecs of commissio coss whe he radig sysems are i acual use. We fid ha he performace of NN model is beer ha he performace of regressio model. We also fid ha for he daily daa, he ARRs of boh regressio model ad NN model are beer ha he ARRs of buy-ad-hold sraegy i esig period (esig period ARR is ). Uforuaely, for he weekly daa, he ARRs of boh regressio model ad NN model are worse ha he ARRs of buy-ad-hold sraegy i esig period. For he predicio of Shaghai Composie Idex volailiy, we ca fid similar coclusio ha he performace of NN model is beer ha he performace of regressio model. For he fuure work, wo aspecs may be cosidered. The firs aspec: i has bee sudied i lieraure ha beer performace ca be achieved by usig sysems comprisig of muliple models. For example, hree or four models could be used wihi each sysem, ad a red classificaio algorihm ca be use o classify he ime series io a larger umber of differe reds. The secod aspec: he ipu daa used for predicios of markes ca be exeded by usig macro-fudameal daa such as ieres rae ad required reserve raio. Such macrofudameal daa may coai useful iformaio which ca be used o predic marke movemes more accuraely. REFERENCES [1] B. G. Malkiel, A Radom Walk Dow Wall Sree, W. W. Noro & Compay, New York ad Lodo, [2] M. H. Pesara ad A. Timmerma, Forecasig Sock Reurs: A Examiaio of Sock Marke Tradig i he Presece of Trasacio Coss, Joural of Forecasig, Vol. 13, No. 4, 1994, pp doi: /for [3] M. T. Michell, Machie Learig, The McGraw-Hill Compaies, New York, [4] M. H. Eg ad Q.-G. Wag, Modelig of Sock Markes wih Mea Reversio, The 6h IEEE Ieraioal Coferece o Corol ad Auomaio (IEEE ICCA 2007), Guagzhou, 30 May-1 Jue 2007, pp [5] J.-H. Wag ad J.-Y. Leu, Sock Marke Tred Predic-

11 Chiese Sock Price ad Volailiy Predicios wih Muliple Techical Idicaors 219 io Usig ARIMA-Based Neural Neworks, The 1996 IEEE Ieraioal Coferece o Neural Neworks, Washigo DC, 3-6 Jue 1996, pp [6] W. Wag, D. Okubor ad F. C. Li, Fuure Tred of he Shaghai Sock Marke, ICONIP '02: Proceedigs of he 9h Ieraioal Coferece o Neural Iformaio Processig, Sigapore, November, pp [7] L.-X. Liu ad J.-H Ma, Mulivariae Noliear Predicio of Shezhe Sock Price, The 3rd Ieraioal Coferece o Wireless Commuicaios, Neworkig ad Mobile Compuig (WiCOM 2007), Shaghai, Sepember 2007, pp [8] S.-H. Che, C.-Q Tao ad W. He, A New Algorihm of Neural Nework ad Predicio i Chia Sock Marke, Pacific-Asia Coferece o Circuis, Commuicaios ad Sysems, PACCS 2009, Chegdu, May 2009, pp [9] X. Xiog, X,-T, Zhag, W, Zhag ad C,-Y, Li, Wave- le-based Bea Esimaio of Chia Sock Marke, Proceedigs of 2005 Ieraioal Coferece o Machie Learig ad Cybereics, Guagzhou, Augus 2005, pp [10] W.-R. Pa, Empirical Aalysis of Sock Reurs Volailiy i Chia Marke Based o Shaghai ad Shezhe 300 Idex, 2010 Ieraioal Coferece o Fiacial Theory ad Egieerig (ICFTE), Dubai, Jue 2010, pp [11] X.-M. Sog ad H.-X. Pa, Aalysis of Chia Sock Marke: Volailiy ad Ifluecig Facors, 2010 Ieraioal Coferece o Maageme ad Service Sciece (MASS), Wuha, Augus 2010, pp doi: /icmss [12] S. Hayki, Neural Neworks: A Comprehesive Foudaio, Preice-Hall, Saddle River, [13] Hp://