_ K ^ HOi thto ICT.rda'06 Proceedings ofICT.rda'06. Hanoi May. 20-21.2006
DU* BAO GIA CHLTNG KHOAN SU* DUNG C6NG NGHE MANG NORON
Forecast Stock market using Neural network
Nguyen Quang Hoan, Hoang Thj Lan Phovng Tom tdt
Bai bao nghien cuu khd nang sit dung mgng noron trong du bao gid chung khoan, v&i thugt hoc Backpropagation cho vi$c dao tgo mgng. Va trinh bay mot sd kit qud thir nghiem cua chucmg trinh mo phong.
Tiir khda: Mgng Noron nhdn tgo, du bdo chirng khodn.
Abstract
This paper is a research on ability to apply Neural network in forecasting Stock market using Backpropagation for training. Finally, paper give out some results of demo program of Forecasting Stock market.
Keywords: Neural network. Forecast stock market.
1. GlOfI THIEU
Bai toan dir bao gia ca thi truang chung khoan da dugrc nghien cuu va ap dung mpt so thj truemg nhu: NewYork, Tokyo, DownJonc.vdi dp chinh xac khoang 68%-* 90% [3,7,5]. 6 Vift nam da co mpt so noi nghien cuu ve d[f bao nhu: Uy ban Chung khoan Nha nuoc...Tuy nhien, thj trudng chung khoan Vift nam mdi thanh lap nen dy bao gia chung khoan se duoc quan tam nhifu hon.
Cac phuong phap thyrc hifn dy bao rat da d^ng, moi phuong phap thich hgrp voi tung bai toan cy the. Mpt sd phuang phap dien hinh da dugc thyc hifn de dy bao nhu:
1.1. Phuranig phap ngoai suy
Phuang phap ngo^i suy la mpt trong nhii'ng phucmg phap don gian nhat diing de dy bdo, CO su dyng so lifu thong ke trong qua khur lam dau vao. Cac so lifu qua khu nay se dupe "fit" theo mpt ham nao dd ho^ie su dyng mang Noron thong minh vdi mpt tryc x la tryc thoi gian, mpt tryc y la cac so lifu qua khur. Cac gia trj trong tuong lai se dugrc dy bio bing each tinh gii trj ciia ham tai cic thoi diem trong tuong lai. Tuy theo ham duac lya
chpn de "fit" so lieu ma ta co cic mo hinh dy bao khac nhau. Cac ham dy bio tieu bieu nhat la ham tuyen tinh, ham mil va ham Logistic tuang ung vdi cic mo hinh dy bao tuyen tinh, mo hinh dy bio ham md va mo hinh dy bao ham Logistic [10,3].
a. Mo hinh tuyen tinh
La mo hinh don gian nhat vdi ham dupc dung de "fit" la ham tuyen tinh: y=mx+b.
Theo phuang phap nay thi hf so m, b se dugc tinh:
"d-O' XZ^XZ^') (1.1)
(E.)(E(^1-(S^)(Z^)
(1.2)b. Mo hinh hdm mH
Mo hinh ham mu pho bien de tinh cic dai lugng tang truong nhu dan so... Phuang trinh cua ham mu dugc hifu dien: y=b*m^x. Lay hai ve ta se co:
Ln(y)=x*ln(m)+ln(b) (1.3) Khi dimg phuong phip binh phuang toi
thifu de "fit " ham tren vdi s6 lifu qua khu di
Ky y^u HQi thto ICT.rda'06 Proceedings of ICT.rda'06. Hanoi.
dugc bien doi tuong umg, ta tim dugc cac hf so a=ln(m) va c=ln(b), m la cic hf so cho vifc "fit" ham tuyen tinh dS neu tren. Khi do ffi va 6 sg dugc tinh:
m—e vam=e (1.4) c. Mo hinh mgng Noron
La mo hinh co kha ning "hpc" tu cac du lifu qui khur, co thf cap nhat cac tham so. Mo hinh m^ng Noron cd thf sir dyng ca ham tuyen tinh va phi tuyen cho dao tao mang.
Neu lya chgn dugc cac tham so tdi uu thi do la mo hinh xap xi rat tot ducmg cong djch chuyen ciia doi tugng can dy bao. Cy the ve nmng Noron s6 dugc trinh bay t^i phan 2.
1.2. Mo hinh kinh te lirgrng
Mo hinh kinh te lugng la mo hinh vdi nhif u bien mo ta sy phy thugc cua cic d^i lugng can dy bio tren co sd cac thong so kinh tf xa hOi. Mo hinh kinh te lugng dugc hifu dien bdi ham sau:
y=a,*x,+a2*X2+ +a„*x„+b (1.5) Trong do y li d^i lugng can dy bio; xj, X2.
X3....x„ la cic thdng so kinh ti xi hgi lien quan. Cic tham s6 aj.a2... a„ xic dinh sy phy thugc cua d^i lugng dy bio vio cic thong s6kinhte-x3hgi[10].
1.3. D^ bio theo hf thong chuyen gia Md hinh chuyen gia dya tren dinh gii ciia chuyen gia trong llnh vyc dy bio. Cic so lifu dy bio cua cic chuyen gia dua ra dugc xem xet, dinh gii va tong hgp de dira ra ket qui dy bio cuoi cimg [3].
Hf thong chuyen gia siir dyng cac lufit cua chuyen gia, va dua ra moi quan hf vdi cac mo hinh toin hgc. Mo hinh chuyf n gia co thf phat trien khi ket hgp vdi mo hinh: Phan tich ket hgp {Conjoint Analysis), Ty moi {Bootstrapping) va Kinh te lugng {Econometric) [10].
Uu diem ciia hf chuyen gia la siir dyng lu^t suy dien de dua ra kft qua. Khi ip dyng hf thong chuyen gia cho thj trudng chumg
khoin thi vifc tfnh toin hay con tri thuc ciia thj trudng li khd chung ta cung chua hieu day dii va cac qui luat ciia chiing. C chuyen gia chi thyc sy tot trong tri thurc cua chiing va khong thyc thdng cd loi va thong tin khong d Trong cic phuang phip tren cd uu diem la: dieu khien dQ I hon, tong hgp va thyc hifn "Cac cd dao t?o". Vi vay, mang noro cho moi trudng thi trudng chung k Bii bao thyc hifn nghien i nghifm mpt phuang phip dy bi mang Noron vdi thuat hgc Backprc 2. iTNG DVNG MANG NORON <
BAO GIA CHUivG KHOAN Tap du* lifu gii ca chiirng khoii thap tren mang Internet tai vahoo.charts.com vdi hon 2000 n chung khoin tir cic nam 1996 den r DH lifu sau khi dugc chuin hoa se vio cho m?ing Noron thyc hifn "1 Sau khi dao t^o mang dugc sur dyng <
Gii chiirng khoin d thdi diem tiep the 2.1. Xfiy dvng cau triic mang
Cau true m?ng BackPropagation cho bai toan li m^ng 3 ldp: mgt ldp >
ldp an va mgt Idp ra. Tuy nhien vifc j mgt clu true toi uu phy thugc vi n ^ i f m ciia nha phit trien, va dya tre thir nghifm [1].
IBnhl: Mgng backpropagation 3 idp
158
HOi thto ICT.rda'06
Hinh 1 mo ta cau triic mang backpropagation 3 ldp. Cd n noron tai idp vio, p noron ldp an, m noron ldp ra, Vj, la trpng so gitla ldp vao va ldp an, wjk la trgng so giOa ldp an va ldp ra ( 0 ^ ^ , 0^^ va
trong do:
X la tip vector dau vao (x/,.. .,x„);
/ la t|p vector dich tai dau ra {t/. ....t„);
Xj la gia trj tai noron dau vao thu i;
Zjla gii trj cua noron diu ra thir k;
tk la gii trj dich cua noron dau ra thii' k.
Dau vao ciia noron ldp an thiiry:
Proceedings of ICT.rda'06. Hanoi May. 20-21.2006
zin.
n
(2.1) /=o
// la ham truyen t^i Idp an, khi dd gia trj ciia noron thury cua ldp an la: z/=fj{zinj).
Dau vao cua Noron ldp ra thu
p
*:>"•«* = Z ^7^ J'' (2.2)
7=0
f2 li ham truyen t^i ldp ra, khi do gii trj ciia noron thir k cua ldp ra la: yk-f2{yink)- Cic him truyen sir dung trong bai toin la ham sigmoid cho ldp an va tuyen tinh cho Idp ra:
1
^'^1 + e-^
f(x)=x
(2.3) (2.4)
2.2. Thuat toan huan luyen lan truyen ngugc (Backpropagation)
Ggi E li sai so binh phuang giira dau ra vi dich ciia mang dang huan luyf n.
Ta CO dg bien doi ciia trpng so Wjk dugc
dE
tinh theo cong thuc p.
Nhu viy t^i mdi md hinh huan luyf n:
VOI:
A _ ^ ^
^Wjk - ~ " T ~ , a la hang sd hpc (2.5) a. Hieu chinh trpng so cho lap ra Wjk
- Ta co:
1 "• '
^ *=o (2.6) dit "*='*">'*. ta cd:
dE _ dE dUk
(2.7) dE
- Tinh : ta cd vdi 2 gii tri M, va Uj 8u^
( / > y ) t h i : — - ^ , va tir(2.6) nen:
2 ^ 5M^ n^ * (2.8)
- Tinh
5W:
(2.9)
^ j k ^ j k ^ j k ^ j k
t, la gii trj dpc lap so vdi trpng so nen:—*-=0 (mpi k=1.2...m)
dw^v (2.10)
nen:
^Jk ^ i k (2.11) nhung ta cd: y, = fiiy'^k) • Do dd:
Ky y^u HQi thto ICT.rda'06 Proceedings of ICT.rda'06. Hanoi May.
Aw =-^—
^ j k ^ j k ^ « * ^ j k ' ykH%,rn vdi
y/ = l,2,„/? (;
^^•^^^ - Tinh dgo hdm f^ix)
p
Ta lai cd: yin, =Y,^j^iy '^'"°"^ chuomg trinh/^ la ham tu
'-' ' ' y=fii.x)=x
(2.13) N
dyin, d ^
Nen: ^^y^^' flW-^
& W j k Vay:
^ * ^ ^ ^ ^ ' ^ ' ' (2.14) • • a& ^ ^ , Nhdn xet: ^^jk = ^ ^ = ^ * ^ y =«(^* ->^^
Cic trpng so trong m?ng Noron dpc lap ^^
dw., riifj-=jork=k- V ^ - l , 2 „ m ^^^^^
nhau, nghia li: g^.^ [Oif j V yor k ^ k' y / = l , 2 „ , / j
6. C^/; n/r^f /ro/t^ so cAo ldp an (2.15) ._ ' " \ u '
^ ^ Ta CO cong thuc:
Suy ra:
Tir cdng thiire (2.6),(2.8),(2.12),(2.15). Ta ^°"-
dE
^ t=i c\ii *=i c^j
= -d^Zy (2.16)
vdi: Sk={tk-yk)f',{yink) (2-22)
(2.17) nhung y, = f2{yin,) nen:
^=_y« ^20^) ^_y.„ M ^ ) ^ = _ y j ^
a^, tr* a.^ t r ' d)in, ^ d^, tr* a.^ ^'-'^^
theo cong thirc (2.14): ym, = 2^Zj^-^^, JL± = ^^ _±
ma v,y va Wjk la 2 bien dpc lap nen: (2.24)
i<cn
gy yiu HQi thto ICT.rda'06 Proceedings of ICT.rda'06. Hanoi May. 20-21.2006
7 _ ff^'ti \ . 2.3. Phan tich v i danh gii cac tham so ciia
nhung Zj-JA^j) nen: mang
&j ^\\Zi^j) _ fif . ^ ^ ^ y Cac tham so inh hudng rat Idn den hieu T " ~ T~ J l ^ J^ p> qua dao tao cua mang noron. Mpt sd tham so
*^ij li iJ (2.25) la: trpng khdi tao, so noron ldp an, each thirc cap nhat trpng, hang sd hpc, dir lieu dau v - ' vao....
Mat khac: j Lu ' u / ) ^ - y^i trpng khdi tgo: thudng dugc (2.26) khdi t^o tai nhirng khoang ngau nhien nhd.
Mot sd cdng thurc de xic djnh khoang trpng trong dd cic bien Vy ddc lap lan nhau nen: nay da dugc dua ra tai tai lieu [10,2,8,6], tuy
^fl nhien khdng dim bao rang do la cic gii trj tdi (2.27) ''"• Trong chuang trinh cac trpng dugc khdi tao ngau nhien trong khoang [-0.5, 0.5]. Cach thurc cap nhat cic tham sd trpng phu thupc vao thuat toan da dugc chpn d tren.
So noron tgi lap dn: khdng duoc qua Idn Tir do ta cd: cung khdng dugc qua it. Vi neu sd noron qua dE -sr^ • 't se dan den ket (jua sai so khi huan luyen kha
—— = ~(^<5]jWj|^yj {zin,))Xj (2.28) Idn, anh hudng den tinh chinh xac cua dy bao.
^ i j *=i Neu sd noron qua Idn thi ket qui sai so thu
*5j dugc cd the be nhung qua trinh hiian luyen se phirc tap va ton thdi gian. So Noron bao nhieu /-'/ . •. la thich hgp chi cd dugc khi thyc hien nhieu 'A:^jl</i^.^"*/ kiem thir. Sau mpt so thu nghifm chuang
*=' trinh chpn so noron cho ldp in la 20.
V?y- Hdng so hpc: cung inh hudng khdng nhe dE ^ tdi hifu qua dao t?io cua mang. 2 hang s6 hgc Av^j =-a—- =-aSjXj (2.29) dugc sir dyng df dinh gii la 0.2 va 0.4.
Sj =E4
dv,
3. CAI DAT VA THU^ NGHIEM - D?o ham fi'(x), vdi f| li ham sigmoid:
z= f(x\= ^ •'•*• ^ * ' ^^* chuong trinh
1 + ^ Chuong trinh dugc viet bang ngdn ngu suy ra: VC++6.0 vdi giao difn iirng dung than thifn
-e-' 1 ., 1
don gian. Cai dat tren may Pentum 111, toe dc {l + e ) l + e i + e QJ^Q jigf, chj^h cua chuong trinh bacgom cic Menu: File, NeuralNetwork, View cd thf vift lai: ^^'P '^ho phep xay dyng cau true mang, die tao mang, thay ddi cac tham sd mang, va thy(
hien du bio.
dE v-*
^v,: = -a-— = -aS^.x. =-a(2^5,w.jZ,(l-z,))j:,
VA = l,2„/?i;V/ = l , 2 „ , «
Ky y^u HQi thto ICT.rda'06 Proceedings of ICT.rda'06. Hanoi M
* anMol «• N*
-riMifira
I j p i I a i U : Urn 1 B i l l :
M i l S^uitt I m i : I k K i M l m t : Irtiaiiii I M V I H : l i U l i t l a B i ^ U t : ItilMkU t u a e t e n : I n l M l l i : htlidi* ItU:
t i i l i d n tirnit;
1 Mi
IteSnla U M I I.U MM l.iMl R.TIM 1M3 iU iU
l:\CK M IK\|kMi| <iVmHiUf«Mt.tl|\M<l (•*Hf4ir, Mltar uT MU It n : » 3M.N1
////lA 2.- C/ao di?n ket qud cua chuong trinh 3.2. Ket qua thurc nghifm
Cic thu nghijim dugc thyc hifn theo nguyen tic: sau mdi lan thyc hien dy bio chuomg trinh cho phep lya chgn so lugng cic dy bio lien tiep nhau theo thdi gian. Vi dy nhu 2 thir nghifm d 3.2.1 thi so dy bio dugc chpn de danh gii la 8, vi the se cd 8 ket qui dy bio lien tiep nhau.
File: Xay dyng mdt clu true m^g mdi - Open: Md mdt clu triic dSl cddldao t90.
- Save as Text File:
Luu k^t qui dudi d^ng file text.
y
3.1.1. Thir nghiem v&i cdc cat nffron idp dn khdc nhau: Cau trt
Dtfbtol Dv bto 2 D^bioB Dybto4 DybtoS Dvbto6 Dybto?
DybtoS
11.294 11.355 11.357 11.381 11.417 11.433 11.456 11.448
11.45 11.21 11.17 10.99 11.2 11.45 11.08 10.94
5000 5000 5000 5000 5000 5000 5000 5000
•
^ 2 2 Sai s6 trung binh:
2,31%
Bang 1: Ket qua thu- nghiem tren 1-20-1
4 Cdu trie 2:10-50-1
D y b i o l Dybio2 b y bio 3 p y b i o 4 by bio 5 by bio 6 Dy bio 7 Dy bio 8
11 11.33 11.303 11.298 11.305 11.291
Modify: Thay doi tnic da ed.
- Stop training: Dim vifc dao t^o - Change Parameter:
Thay ddi cic tham s^
m?ing.- Forecast: Thy hifn du bio
11.284 11.08 5000 776,336 11.243 10.94 5000 776,336
Sai so trung binh:
1,673%
Hinh 4: Menu Neural Nets
Bang 2: Ket qua thiir nghifm tren cau 10-50-1
Qua 2 bing ket qui d tren ta cd nh|i neu so noron Idp in cang Idn thi thdi giai tao mang cang tang. Sd noron cung khdn qua it, qua cic thir nghifm 20 noron la i hgp.
Thij nghifm vdi cae hing so hgc I nhau:
162
, fjfti thto ICT.rda'06
Sai Thiri gian s6(%) (s)
Proceedings of ICT.rda'06. Hanoi May. 20-21.2006
1000 2.53 152.61 1000 1,768 231,142 5000 2.87 775.86 5000 3,57 876,671 28600 2.62 8131.82 10000 3,20 8342,341
"^7600 2.61 6168.99 35000 2^80 7685,540
Bang 2: Ket qua thiir nghiem vdi cac hiing so hoc khac nhau
Theo tren ta thiy hang so hgc cung anh hudng ^cn hifu qui dao tao ciia mang. Vdi hing sd hgc 0,2 thi thdi gian dao tao mang it hon, Idi nhd ban. Nhu vay cd the ndi vdi eau true m?ng 10-20-1, hing sd hpc bing 0,2 la thich hgp.
4. K£T LUAN
Dy bio gii ca chirng khoin la bai toan da dugc nghien ciru nhieu tren the gidi. Vdi kha ning md hinh hoi cic ham phi tuyen, Noron trd thanh mpt cdng cy khi i p dung cho dy bio. Tuy nhien hifu qui cua md hinh cdn phy thudc vio cau true m?ng, cic tham sd dao tao Bii bio chi dirng lai d vifc nghien ciiru bai toin dy bio va thir nghifm mpt phucmg phip in^ng Noron cho dy bio gii chiirng khoin. Ket qui d^t dugc la xay dyng cau true chucmg frinh cho dy bao va mdt so ket qui thyc nghifm, tuy nhien dp chinh xic cua ket qua dy bio chua cao.
De ting dp chinh xic, hifu qui ciia dy bio hudng nghien curu tiep theo cd the ket hgp ni^ng Noron vdi mpt so cdng nghf khic nhu:
Six dyng Logic md de danh gii dau ra dy bio ciia m^ng, sir dyng thu|t toin di truyen de tdi uu qui trinh huan luyf n, sir dyng cic md hinh hf chuyen gia...[4,5].
Tki lifu tham khao
[1] Andrew Nicola Edmonds, Time Series Prediction Using Supervised Learning and Tools from Chaos Theory. December, 1996.
[2] Brian O'Rourke, Neural Nets Forecast
Futures Prices.
wtvw.fenews.eom/fen3/netira/./7fm/.
February 1998.
[3] David S. Walonick, An Overview of
Forecasting Methodology.
www, statpac. com/research- papers/forecastina. htm. 1994.
[4] J. Scott Armstrong, On Calculating die Value of Improved Forecast Accuracy. The Wharton School, University of Pennsylvania Philadelphia, Pa. 19104. www- marketinq.wharton.upenn.edu/ideas/pdf/v alue.pdf. 1985.
[5] JingTao YAO, Chew Lim TAN, A Study on Training Criteria for Financial Time Series Forecasting.
www2.cs.ureqina.ca/~itvao/Papers/criteri a iconip01.pdf. 1998.
[6] Joyce Berg, Forrest Nelson and Thomas Rietz, Accuracy and Forecast Standard Error of Prediction Markets, Departments of Accounting, Economics and Finance. July 2003.
[7] Marijana Zekic, Neural Network Applications in Stock Market Predicdons- A Methodology Analysis. University of Josip Juraj Strossmayer in Osijek Faculty of Economics Osijek Gajev trg 7, 31000 Osijek Croatia.
www.efos.hr/nastavnici/
mzekic/radovi/mzekic varazdin98.pdf.
1998.
[8] Ramon Lawrence Department of Computer Science University of Manitoba, Using
Neural Networks to Forecast Stock Market Prices. December 12, 1997.
[9] Robert Dorseyi and Randall Sextonii, The Use of Parsimonious Neural Networks for Forecasting Financial Time Series. Department of Economics, Conner Hall, University of Mississippi. 1998.
[10] Trin Duy Dung, Phit triin phin mem dy bao nhu ciu djch vy cho mang viln thong Vift nam su dyng cong nghf Tri tuf nhan t?o). Ma s6: 087-2001- TCT- RDP- TH- 67, Vifn Khoa hpc Ky thuat Buu difn. 2001.
Ky ylu HQi thto ICT.rda'06 Proceedings of ICT.rda'06. Hanoi May. 2
Ve cde tic gia
Phd Gito su Nguyin Quang Hoan t6t nghiep D^i hpc nganh KT thuSt m4y tinh va T^r dpng o Moskva nam 1973, nhan bang Ti6n sJ nam 1997, nhSn hoc ham Pho gito su nam 2001. Tir nam 1974 dfin 1998 la can bp nghien curu 6 Vien Cong nghe Thong tin, Vi?n Khoa hpc-Cong ngh? Vi?t nam. Tir nam 1998 dfin 2006 la Tnrong khoa C6ng ngh? Th6ng tin I, Hpc vi?n Cong nghe Biru chinh Vien thong; nay la tnrdng Trung tim Thong tin Thir vi^n, Hpc vi?n Cong ngh§ Buu chinh Viln th6ng. Nhitng vin dh quan tam nghien ciiru ciia Ph6 Giio su Hoan la Mang no ron nhan tao, Trt tu^ nhdn tao
Hoang Thj Lan sinh nam 1983, hit vien nam cuoi kl nghf Thong tin- Cong nghf Buu cl thong. Nhihig qi nghien ciru ciia tic gom: Mang Noron ThiSt ki mang.
Email: [email protected].
