Using Neural Networks And Genetic Algorithms To Predict Stock Market Returns

166 Pages·2001·1.08 MB·English

by  Efstathios Kalyvas

U CITENE GDN ASKROWTE NLARUE NGNIS
SNRUTE RTEKRA MKCOT STCIDER PO TSMHTIROGLA

A U M
THESIS SUBMITTED TO THE NIVERSITY OF RETSEHCNA
M S
ROF EHT EERGED FO RFEOTSA CIENCE
A C S
IN DECNAVD OMPUTER CIENCE
F S E
IN THE FYOTLUCA EC NDENIAC NGINEERING

B y
savyla KsoihtatsfE
ecne irceStup mtfonOCemtrapeD
October 2001
C ontents

Abstract 6
Declaration 7
Copyright and Ownership 8
Acknowledgments 9

1 Introduction 11

1.1 smiAs edvniatcejbO ................................ ................................ ........................ 11
1.2 elanoitaR ................................ ................................ ................................ ......... 12
1.3 kcotS tekrnaoMitciderP ................................ ................................ .................. 12
1.4 no ift oyaedzhuittnSagrO ................................ ................................ ................ 13

2 Stock Markets and Prediction 15

2.1 ehT kcotStekraM ................................ ................................ ............................ 15
2.1.1 tnemtsevsneIiroehT ................................ ................................ ..................... 15
2.1.2 de taoatt tlaeeeDkhRrtaM ................................ ................................ .......... 16
2.2 noitc ifdoer PehttekraM ................................ ................................ .................. 17
2.2.1 gninife Denhotitcidkesrapt ................................ ................................ ......... 17
2.2.2 sI ?eehl tbtaetkcriadMerp ................................ ................................ ........... 18
2.2.3 noitciderPsdohteM ................................ ................................ ..................... 19
2.2.3.1 lacinhsciesTylanA ................................ ................................ ............... 20
2.2.3.2 lsaitsnyelmaandAnuF ................................ ................................ ......... 20
2
2.2.3.3 lann ooe iimsttieciTiidrdaeerSrTP ................................ ...................... 21
2.2.3.4 gsendnioinhhrtcaeaeMML ................................ ................................ . 23
2.2.3.4.1 tserNaeN robhgieseuqinhceT ................................ ...................... 24
2.2.3.4.2 lsakrruoewNteN ................................ ................................ .......... 24
2.3 kgr non i woenfkeihiOsmtT faacreTriuDFdOerP ................................ ........... 35
2.3.1 noi tfcoi dteeehrktP r nayoMsliisaadB ................................ ....................... 35
2.3.2 gnnoi ikne tsihtcaftciTeadDxeErP ................................ ............................. 37
2.3.3 no iltecdeolMeS ................................ ................................ ........................... 38
2.3.4 ataDnoitceleS ................................ ................................ ............................. 39

3 Data 41

3.1 gnid naattasDrednU ................................ ................................ ........................ 41
3.1.1 nloai itatctieanlDIloC ................................ ................................ ................. 41
3.1.2 ataDnoitpircseD ................................ ................................ ......................... 42
3.1.3 y taitlaaDuQ ................................ ................................ ............................... 43
3.2 ataDnoitaraperP ................................ ................................ ............................ 44
3.2.1 ataDnoitcurtsnoC ................................ ................................ ....................... 44
3.2.2 n oaittaaDmroF ................................ ................................ ........................... 46
3.3 gnitseT rsosFenmodnaR ................................ ................................ ................. 47
3.3.1 Randomness ................................ ................................ ................................ 47
3.3.2 tnsueRT ................................ ................................ ................................ ...... 48
3.3.3 tSsDeBT ................................ ................................ ................................ ..... 51

4 Models 55

4.1 lanoi teimdgi anTsrieTtisraecSeroF ................................ ............................... 55
3
4.1.1 eta ierta a vndirionraniaaeUsvnsiietlrlgueMr ................................ ............. 55
4.1.2 a inerose iU totei tanrfm iCo rmfeeouehrfmd tunigtItacpluorts .................. 57
4.1.3 noita ufloav Eeht RAledom ................................ ................................ ......... 58
4.1.4 gni ks cleeahhutC driosfernon - sr ertateanpil ................................ .............. 60
4.1.5 Software ................................ ................................ ................................ ...... 61
4.2 la ilcsaikrfruioetwNrtAeN ................................ ................................ .............. 61
4.2.1 noitpircseD ................................ ................................ ................................ . 61
4.2.1.1 snorueN ................................ ................................ ............................... 62
4.2.1.2 sreyaL ................................ ................................ ................................ . 62
4.2.1.3 tn esmtthsguijedWA ................................ ................................ ............. 63
4.2.2 sretemaraPgnitteS ................................ ................................ ...................... 72
4.2.2.1 snorueN ................................ ................................ ............................... 72
4.2.2.2 sreyaL ................................ ................................ ................................ . 72
4.2.2.3 tn esmtthsguijedWA ................................ ................................ ............. 73
4.2.3 cistmehnteiGroglA ................................ ................................ ...................... 74
4.2.3.1 noitpircseD ................................ ................................ .......................... 74
4.2.3.2 A lanoitnev ncoiCteneGmhtiroglA ................................ ...................... 74
4.2.3.3 A sAe eGr tnuea ithhsfctt’euNDrNtS ................................ .................. 77
4.2.4 no ift oaeuhl tlaNevNdEom ................................ ................................ ......... 81
4.2.5 Software ................................ ................................ ................................ ...... 81

5 Experiments and Results 82

5.1 tnemir e enp:voxIiiEsts sclgeienrddigeoserMUrPotuA ................................ ... 82
5.1.1 noitpircseD ................................ ................................ ................................ . 82
5.1.2 noitaci lfpop Aekiak A nd oanniiaatriaesmteriyorafCBnI ............................ 83
4
5.1.3 AdjMuosdtemle AnRt ................................ ................................ .................. 84
5.1.4 no ift oaeuhls talRveAEdom ................................ ................................ ........ 84
5.1.5 Investigating for Non - srlaaeundiilseR ................................ ........................ 86
5.2 tnemirepxE n os gi klnt:raicIorsiIwuUdteeeNrNP ................................ .......... 88
5.2.1 noitpircseD ................................ ................................ ................................ . 88
5.2.2 hcr agenSisU e hmcthittiernoegGlA ................................ ............................ 90
5.2.2.1 FTSE ................................ ................................ ................................ ... 92
5.2.2.2 P&S ................................ ................................ ................................ ... 104
5.2.3 nosi kt frctoo eswelethetetStNif ................................ ................................ . 109
5.2.4 nsoki rtftoao swueetlhteattNviEf ................................ .............................. 112
5.2.5 nois sf uostc ensemeiho mDtcfiItorIueopxE ................................ ............. 114
5.3 snoisulcnoC ................................ ................................ ................................ .. 115

6 Conclusion 118

6. 1 stluse Rf oyrammuS ................................ ................................ ....................... 118
6.2 snoisulcnoC ................................ ................................ ................................ .. 119
6.3 erutuFkroW ................................ ................................ ................................ .. 120
6.3.1 tupnIataD ................................ ................................ ................................ . 120
6.3.2 nrettaPnoitceteD ................................ ................................ ...................... 121
6.3.3 esniooiNtcudeR ................................ ................................ ........................ 121

Appendix I 122
Appendix II 140
References 163

5
A bstract

nI siht yduts ew tpmetta ot tciderp eht yliad ssecxe snruter fo ESTF 005 dna P&S 005
secidni revo e hetvi tycreupssaeerr TlliB eta r. s,nyrlultaeirtin Iew evorp taht eht ssecxe
snruter emit seires od ton etautyclumlofdnar er o.mre hetwr uyFlpp a tonwe trseefpfyitd
fo noitciderp :sledom evissergerotuA )RA( dna deef drawrof larueN skrowteN )NN( ot
tcide reph ts ssencrxue teemr istei rgens ids eu.gsgeaull arvoF eht NN sledom a citeneG
mhtiroglA si detcurtsnoc ni redro otsoohc e . eyymhlgutlomalinot ippeeFootwtaulave
n oe ihsttlceiddoemr pno tr nuseocrfierftf e idmedndaul ctnaohct yeht od to neganam ot
.esaïvr otciderpnf oseitilib anoitcider peh tyltnacifingi smrofreptuo

6
D eclaration

No portion of the krow derrefer ot ni eht siseht sah neeb dettimbus ni troppus fon a
noitacilppa rof rehtona eerged ro noitacifilauq fo siht ro yna rehto ytisrevinu ror ehto
.gninra e fleotutitsni

7
C opyright and O wnership

thgirypoC ni txet fot sih siseht stser htiw eht .rohtu AseipoC yb( yna )sseco rrpehtie ni
,lluf ro fo ,stcartxe yam eb edam ylno ni ecnadrocca htiw snoitcurtsni nevig ybe ht
rohtuA dna degdol ni eht nhoJ sd nyatliysRr eyvrianrUbiL f o.rets eshlcinaatMeDy am
eb deniatbo morfeht .nairarbil sihT egap tsum mrof trap fo yna hcus seipoc. edam
rehtruF seipoc yb( yna )ssecorp fo seipoc edam ni ecnadrocca htiw hcuss noitcurtsni
.roht ueA h ft)ogniti rnw in(oissimr etpuoht iewd a emtb oynam

ehT pihsrenwo fo y nlaautc eyltlreetpnoirpgir sth hcihw yam deebbirc sneid sihs tiseht
si detsev ni eht ytisrevinU fo ,retsehcnaM tcejbus ot yna roirp tnemeerga ot e ht
,yrartnoc dna yam ton eb edam elbaliava rof esu yb driht seitrap tuohtiwn ettirw
noissi mfro e,py t eihhsctri eh evwlbilinirUwcsertp eh smret sdnnoaitid nfooc yna hcus
.tnemeerga

rehtruF noitamrofni no eht snoitidnoc rednu hcihw serusolcsid dna noitatiolpxey am
.ecnei crSetupm oftConemtrap ee D hfdtoa ee Hhmto reflbalia vseaica lepkat

8
A cknowledgments

I dl ueokwilt rdoi sv nsa., oeDSrsif ostoo osear tsiriPyk vpcmnydrxeamneerhapptupsa
,eérB rof sih elbaul aevcivda dna ,ecnadiug dna ym edutitarg ot roin erserutc enLahtaN
.L ,hpesoJ rof sih suounitnoc troppus dna .ecnatsissa I dluow osla ekil ot knahtm ihaR
akkaLP( .stnemm ogcninethgil nde npal esh irh o)ftnedu t.SD.h

I deen sa llew ot kn aehttirdna moirhocfr iAakkiaNregorpsA rof si hlacigolohcyspd na
laicnanif .troppus tsaL tub ton tsael I dluow ekil ot knaht ym ytisrevinUs rehcaet
itoiganaP inaigodoR dna adinoeL oilaP rof rieht pleh dna ecivda ta eht laitini egatsf o
.sei deuttasudarg tysmop

.elbisa enfe eebv adhlu okwr otwnerr ue c hfeton oenlpo eepse hl t lfpaol ee hhttuohtiW

. . ò ò.ù ßòé«ëõåõå ßïíïò ïåìïôõíôöãï ßùë òå áòéæêõå óïõÜôïä ìïô ». íõßååæ
9
D edication

oT ym stnerap sorteP dna ,airaM ohw deveileb ni em dna doots yb ym edis lla eht yaw
d n,a s .ariys i elmdrs hytineud pmssehontoisitiasSsarocoVfrembrp

,.o oe Tttuo enrqheyleyiwdrgmMnaenoumvimes

10