Forecasting Stock Price Index Using Artificial Neural Forecasting Stock Price Index Using Artificial Neural Networks in the Indonesian Stock Exchange.
THESIS
Forecasting Stock Price Index Using Artificial Neural
Networks in the Indonesian Stock Exchange
SOUKKHY TIPHIMMALA
Sdut.Id: 125001870/PS/MM
PROGRAM STUDY MASTER MANAGEMENT
PROGRAM GRADUATE
UNIVERSITY OF ATMA JAYA YOGYAKARTA
2014
INTISARI
Indeks harga saham adalah faktor yang signifikan mempengaruhi awal pada
pengambilan keputusan keuangan
keua
uang
ngan investor. Itu
Itu sebabnya memprediksi gerakan
yang tepat dari indeks
indeeks harga saham jauh dianggap. Penelitian
Pene
neli
l tian ini bertujuan untuk
mengevaluasi
penggunaan
indikator
mengevalua
asi efektivitas pengg
gun
unaa
aann in
indi
dika
kato
or teknis, sepertii A / D Oscillator,
Moving
ng Average, RSI,
RSI, CCI,
CCI, MACD, dll dalam
dala
l m memprediksi
memp
me
mprediksi pergerakan
perger
erakan Bursa
Efek
Indeks
Harga
jaringan
syaraf
digunakan
Ef
fek Indek
ekss Ha
Har
rga Indonesia
In
ndo
donesia (BEI). Sebuah jarin
inga
gan sy
yar
araf
af ttiruan
i uan di
ir
igu
g nakan
Yahoo.Finance.
untuk
k peramalan
pera
pe
ram
malan
n indeks harga saham. Data yang ada dicapai
ai darii Ya
Yaho
hoo.
o Finan
ance.
Untuk
tingkat
indeks
Un
Untu
t k menangkap
mena
nangkap hubungan antara indikator teknis dan tingka
at inde
eks di pasar
pasaar
untuk
digunakan.
Kinerja
un
untu
tuk periode
pe
diselidiki, jaringan saraf propagasi kembali diguna
nakann. Ki
Kin
nerja
menunjukkan
statistik
k dan keuangan dari teknik ini dievaluasi dan hasil empiris m
enunjukka
kann
memprediksi
bahwaa jaringan syaraf tiruan adalah alat yang cukup baik untuk m
empreedikssi
pasarr
kkeuangan.
euanggan
an.
kunci:
teknis,
jaringan
Kata k
Kata
unci: Peramalan, prediksi, indeks harga saham, indikator tekn
knis
is, ja
jar
ring
ngan
syaraf
sy
syar
araf
a tiruan
ii
ABSTRACT
Stock price index is the initial
al significant
sig
ignificant factor
faccto
torr influencing on investors' financial
decision making. That's
movements
Tha
hat's why predicting the exact movem
ements of stock price index
is considerably
aims
considerabl
bly regarded. This sstudy
t dy aim
tu
imss at evaluating
eva
valuating the effectiveness
effe
fect
c iveness of using
technical
Moving
Average,
CCI,
technica
cal indicators, such
such as
as A/D Oscillator, Mo
M
ving
vi
ng A
verage, RSI, C
CI, MACD,
etc.
predicting
movements
Price
ettc. in pred
edic
icti
ting
ng movem
ements of Indonesian Stock
ck Exchange
Exchang
nge Pr
Pric
ice Indexx (IDX).
An artificial
neural
forecasting.
Thee
art
rtif
ific
icia
ial ne
eural network is employed for stock price iindex
ndex for
nd
orec
ecas
asti
t ng. Th
T
existing
ex
xis
isti
ting data
dat
ata are achieved from Yahoo.Finance. To capture the rrelationship
elat
el
a ionshiip
between
market
betw
be
tween
n the technical indicators and the levels of the index in the m
ark
ket ffor
or the
period under investigation, a back propagation neural network iss used.
usedd. The
The
statistical
and
empirical
statistiical and financial performance of this technique is evaluated an
nd emp
piricaal
results
artificial
result
ltss revealed that ar
arti
tifi
fici
cial
al neural
neural networks
ks aare
re ffairly
airl
ai
rlyy good tools for
for financial
financi
cial
al
market
m rket predicting.
ma
Keywords:
Forecasting,
indicators,
Keyw
Ke
ywords: F
orecaast
stin
ing
g, prediction,
predi
dict
ctio
ion,
n, stock
sto
tock
ck price iindex,
ndex
nd
e , ttechnical
ech
hnical
i
indi
in
dica
cators,
artificial
networks
artifi
fici
cial
al neurall ne
net
tworks (ANN)
iii
ACKNOWLEDGEMENTS
I would like to express my
y ssincere
inccere thanks
in
thank
nkss and
a d appreciation to my supervisor,
an
Sukmawati
Professor Dr. J. Suk
kmawati Sukamulja, for her valuable
le aadvice,
dvice, guidance and very
study
Master
kind supportt from
from the beginningg of my
my st
stud
udyy att Faculty of Mast
ster
e of Management
until my graduation.
My
Felix
M
y gratitude
grati
titu
tude
de to
to Drs.
s. F
elix Wisnu Isdaryadi, MBA for
for his sincere
sinc
ncer
eree comments
commen
ents for
final
the fi
fina
nall ed
eedition
itio
on of this thesis.
iv
Table of Contents
DECLARATION ................
.........................................................................................................i
.........................................................................................
INTISARI ...................................................................................................................ii
...................................................................................................................
ABSTRACT
ABSTRA
ACT ..............................................................................................................iii
.............................................................................................................................
AC
CKNOWLE
LEDG
D EM
MENTS
S ........................................................................................iv
..............................................................................................
ACKNOWLEDGEMENTS
Tabl
Ta
bles ...
...............................................................................................................
Listt off Tables
............................................................................................................vii
List of
of Figures
Figu
gures ..........................................................................................................viii
...............................................................................................................v
List
ABBR
AB
REVATIONS .....................................................................................
................
ABBREVATIONS
....................................................................................................ix
CHA
APTER 1 INTRODUCTION ................................................................................
................................................................................ 1
CHAPTER
11.
1. Problem
P oblem Identification..........................................................................
Pr
.............. 5
1.1.
.........................................................................................
1.2. Objective off the Researchh ....
..................................................................................... 6
...................................................................................
11.
4. Scope of the Research .........................................................................................
............................................................................................ 8
1.4.
Organization
..................................................................................
1.5 Orga
1.5.
nizati
i tion of
of the
the Thesis ...
........................................................................................... 9
CHAP
APTE
TER
R 2 LITERATURE
LI
REV
EVIEW ................................................................... 10
CHAPTER
REVIEW
2.1 Artificial Neural Network ..................................................................................
................................................................................. 10
researches
..........................................................................
2.2 Review of previous researche
hes ........
................................................................... 11
2.3 Learning Paradigms in ANNs ....
........................................................................ 14
...........................................................................
CHAPTER 3 RESEARCH METHODOLOGY ...................................................... 20
3.1 Statistical Performance Evaluation of the Model.............................................. 22
v
3.2 Financial Performance Evaluation of the Model .............................................. 24
3.3 Research Data................................................................................................... 25
3.4 Data preparation ...
............................................................................................... 26
...............................................................................................
Calculation.........................................................................................
3.5 Variablee C
alculation.......................................................................................... 27
CHAPTER
DESCRIPTIVE
CHAP
PTER 4 DES
SCR
CRIP
IPT
TIVE STATISTICS
STA
TATI
TIST
STIC
CS ..........................................................
...................................................................... 31
CHAPTER
RESEARCH
CH
CHAPTE
ER 5 R
ESEARC
RCH
H RESULTS AND ANALYSIS
AN
NAL
ALYSIS .....................................
.......................................... 36
5.1 Comparison
Com
ompariso
son of Financial Performance..............................................................
Performance..................................................................... 36
.............................................................
5.2 Comparison
5.2
Com
mparison of Statistical Performance ...................................
................................ 45
CHAPTER
CH
C
AP
PTER 6 CONCLUSION ..................................................................................
...................................................................................... 49
REFERENCES
REFE
ERENCES .........................................................................................................
............................................................................................................ 54
Apendix
code...........................................................................................
Ap
Apen
endix A: Matlab code.......................................................................
......................... 58
...................................................................................................
A. Preprocess code ..........................
............................................................................... 58
Training
.......................................................................................................
B. Tr
B
Trai
aini
ning
ng code ............................................................................
.................................... 60
C. Testing
Tes
esting cod
ode.
e........................................................................................................................... 73
code.........................................................................................................
vi
List of Tables
Table 1. The number off ssample
ample in the entire data
ta sset
et ............................................... 26
Selected
indicators
........................................
Table 2. Selec
cte
ted
d technical indi
d cators and their formulas ......
.................................... 28
Variables
.......................................................................................
Table 3. Defined V
aria
ar
iabl
bles
e .........................
............
.............................................................. 30
ANN
parameter
.....................................
Ta 4. A
Table
NN pparameter
arametter llevels
evels tested in paramete
terr setting ........
.................................... 32
Tabl
blee 5.
5 S
umm
mary statistics for the selected indicators ..........
......................................... 33
Table
Summary
............................................
Ta
Tabl
b e 6. T
hree parameters for training and testing of ANN mode
el ......
............................ 37
Table
Three
model
..........................
Ta
T
ble 77:: Testing with parameter combination (10, 0.2 , 0.5, 1e6) ...................
............. 38
Table
............................
Tablee 8. Testing with parameter combination (30, 0.3, 0.5, 1e6) ......................
.......... 39
.............................
Ta
Tabl
b e: 9. Testing with pparameter
arameter combination ((50,
50,, 0.2, 0.5, 1e-6) ......
........................ 39
Table:
..........................
Table 10. Summary of the best fore
reca
c st
stin
ing, parameters (10, 0.2 , 0.5, 1e6) ...
............. 41
forecasting,
............
Ta
Tabl
blee 11
11. Financial pe
pperformance
rformance of ANN model ...........................
................................... 42
Table
.....................................................
Ta
Tabl
blee 12. The em
empi
pirica
i al re
resu
sult
lt of ot
othe
herr rresearch
esearrch .....
......................................................... 44
Table
empirical
result
other
......................................................
Table: 13 the best statistic & financial
fina
nancial performance
peerf
r ormance ............................................... 46
Table 14. Statistical performanc
ce of ANN m
odel .................................................... 48
performance
model
vii
List of Figures
Fig. 1 An artificial neural
neurral network is an interconnected
interconn
nnected group of nodes................. 11
Fig. 2 A Neura
Neural
..........................................
rall network with
h three-layer feed forward ........
.................................... 16
Fig. 3 Tan-Sigmoid
Function
Linear
Transfer
Function
Tan-Sigmoid
id Transfer
Tra
ran
nsfer Fu
Func
ncti
tion
on and
nd Li
Line
near
ar T
r nsfer Func
ra
cti
tion ................. 31
Fig.
Data
preparation
normalized
technical
Fi 4 Dat
ataa pr
prep
eparation
n (actual
(a
technical parameters
parameete
ters
r & nor
rma
mali
lize
zed techni
nical
parameters)
......................................................................................................
para
pa
ram
meteers
rs) ..................................................................
......................................... 34
Fig.
...................................................................
Fig
Fi
g. 5 Training
Tra
raining process of ANN model ........................................
............................... 34
Fig.
..................................................................................
Fi 6 Testing of ANN model ............................................................
........................... 35
Fig.77 Predict next trading day, by entering new data to the network ................
......................
........ 35
Fig.
Fig 8 Training & Forecasting performance (%) of ANN model for a whole
whoole data
datta
set (n = 50
..........................................................
50, η = 00.2,
.2
2, μ = 00.5,
.5,, ep = 1e6).
.5
1e6
e6)). ..................................................
............ 41
..............
Fig. 9 Forecasting performance (%) of ANN model for various η values
Fig.
es ...
.............. 43
viii
ABBREVATIONS
GDP : gross domestic product
prodduc
uctt
IA
: artificial
artificiaal iintelligent
ntelligent
ANN : artificial neur
u al nnetwork
etwork
k
neural
ID
DX
IDX
In
ndo
done
nesi
sian Sto
tock
ck Index
: Indonesian
Stock
JKSE
E : Jakarta
Jakar
artta Stock Exchange (Pervious name of IDX)
MAE : m
MAE
ean absolute error
mean
RMSE
E : root mean square error
MA E : mean absolute percentage error
MAPE
R2
: goodness of fit
APE
APE
abso
solu
lute
te ppercentage
erce
er
cent
ntag
agee er
erro
r r
: ab
absolute
error
PO
O
pre
redi
dict
cted
ed ooutput
utp
ut
put
: predicted
AO
: actual output
CCI
x
: commodity channel index
MACD: moving average convergencee divergence
divergence
ROC : price-rate-of change
RSI
: relative strength index
ix
PR
: predicted rate (forecasting rate)
n
: neuron
η
: learning rate
μ
constant
: momentum co
ep
: epoch
h
IT
informationn technology
tec
echn
hnologyy
: information
LSM
M : The Li
Liby
byan Exchange
Exchang
ngee St
Stockk Ma
rkkett
Libyan
Market
T
EPIX : The
Thee Tehran
T hran
Te
an Exchange Price Index
TEPIX
x
Forecasting Stock Price Index Using Artificial Neural
Networks in the Indonesian Stock Exchange
SOUKKHY TIPHIMMALA
Sdut.Id: 125001870/PS/MM
PROGRAM STUDY MASTER MANAGEMENT
PROGRAM GRADUATE
UNIVERSITY OF ATMA JAYA YOGYAKARTA
2014
INTISARI
Indeks harga saham adalah faktor yang signifikan mempengaruhi awal pada
pengambilan keputusan keuangan
keua
uang
ngan investor. Itu
Itu sebabnya memprediksi gerakan
yang tepat dari indeks
indeeks harga saham jauh dianggap. Penelitian
Pene
neli
l tian ini bertujuan untuk
mengevaluasi
penggunaan
indikator
mengevalua
asi efektivitas pengg
gun
unaa
aann in
indi
dika
kato
or teknis, sepertii A / D Oscillator,
Moving
ng Average, RSI,
RSI, CCI,
CCI, MACD, dll dalam
dala
l m memprediksi
memp
me
mprediksi pergerakan
perger
erakan Bursa
Efek
Indeks
Harga
jaringan
syaraf
digunakan
Ef
fek Indek
ekss Ha
Har
rga Indonesia
In
ndo
donesia (BEI). Sebuah jarin
inga
gan sy
yar
araf
af ttiruan
i uan di
ir
igu
g nakan
Yahoo.Finance.
untuk
k peramalan
pera
pe
ram
malan
n indeks harga saham. Data yang ada dicapai
ai darii Ya
Yaho
hoo.
o Finan
ance.
Untuk
tingkat
indeks
Un
Untu
t k menangkap
mena
nangkap hubungan antara indikator teknis dan tingka
at inde
eks di pasar
pasaar
untuk
digunakan.
Kinerja
un
untu
tuk periode
pe
diselidiki, jaringan saraf propagasi kembali diguna
nakann. Ki
Kin
nerja
menunjukkan
statistik
k dan keuangan dari teknik ini dievaluasi dan hasil empiris m
enunjukka
kann
memprediksi
bahwaa jaringan syaraf tiruan adalah alat yang cukup baik untuk m
empreedikssi
pasarr
kkeuangan.
euanggan
an.
kunci:
teknis,
jaringan
Kata k
Kata
unci: Peramalan, prediksi, indeks harga saham, indikator tekn
knis
is, ja
jar
ring
ngan
syaraf
sy
syar
araf
a tiruan
ii
ABSTRACT
Stock price index is the initial
al significant
sig
ignificant factor
faccto
torr influencing on investors' financial
decision making. That's
movements
Tha
hat's why predicting the exact movem
ements of stock price index
is considerably
aims
considerabl
bly regarded. This sstudy
t dy aim
tu
imss at evaluating
eva
valuating the effectiveness
effe
fect
c iveness of using
technical
Moving
Average,
CCI,
technica
cal indicators, such
such as
as A/D Oscillator, Mo
M
ving
vi
ng A
verage, RSI, C
CI, MACD,
etc.
predicting
movements
Price
ettc. in pred
edic
icti
ting
ng movem
ements of Indonesian Stock
ck Exchange
Exchang
nge Pr
Pric
ice Indexx (IDX).
An artificial
neural
forecasting.
Thee
art
rtif
ific
icia
ial ne
eural network is employed for stock price iindex
ndex for
nd
orec
ecas
asti
t ng. Th
T
existing
ex
xis
isti
ting data
dat
ata are achieved from Yahoo.Finance. To capture the rrelationship
elat
el
a ionshiip
between
market
betw
be
tween
n the technical indicators and the levels of the index in the m
ark
ket ffor
or the
period under investigation, a back propagation neural network iss used.
usedd. The
The
statistical
and
empirical
statistiical and financial performance of this technique is evaluated an
nd emp
piricaal
results
artificial
result
ltss revealed that ar
arti
tifi
fici
cial
al neural
neural networks
ks aare
re ffairly
airl
ai
rlyy good tools for
for financial
financi
cial
al
market
m rket predicting.
ma
Keywords:
Forecasting,
indicators,
Keyw
Ke
ywords: F
orecaast
stin
ing
g, prediction,
predi
dict
ctio
ion,
n, stock
sto
tock
ck price iindex,
ndex
nd
e , ttechnical
ech
hnical
i
indi
in
dica
cators,
artificial
networks
artifi
fici
cial
al neurall ne
net
tworks (ANN)
iii
ACKNOWLEDGEMENTS
I would like to express my
y ssincere
inccere thanks
in
thank
nkss and
a d appreciation to my supervisor,
an
Sukmawati
Professor Dr. J. Suk
kmawati Sukamulja, for her valuable
le aadvice,
dvice, guidance and very
study
Master
kind supportt from
from the beginningg of my
my st
stud
udyy att Faculty of Mast
ster
e of Management
until my graduation.
My
Felix
M
y gratitude
grati
titu
tude
de to
to Drs.
s. F
elix Wisnu Isdaryadi, MBA for
for his sincere
sinc
ncer
eree comments
commen
ents for
final
the fi
fina
nall ed
eedition
itio
on of this thesis.
iv
Table of Contents
DECLARATION ................
.........................................................................................................i
.........................................................................................
INTISARI ...................................................................................................................ii
...................................................................................................................
ABSTRACT
ABSTRA
ACT ..............................................................................................................iii
.............................................................................................................................
AC
CKNOWLE
LEDG
D EM
MENTS
S ........................................................................................iv
..............................................................................................
ACKNOWLEDGEMENTS
Tabl
Ta
bles ...
...............................................................................................................
Listt off Tables
............................................................................................................vii
List of
of Figures
Figu
gures ..........................................................................................................viii
...............................................................................................................v
List
ABBR
AB
REVATIONS .....................................................................................
................
ABBREVATIONS
....................................................................................................ix
CHA
APTER 1 INTRODUCTION ................................................................................
................................................................................ 1
CHAPTER
11.
1. Problem
P oblem Identification..........................................................................
Pr
.............. 5
1.1.
.........................................................................................
1.2. Objective off the Researchh ....
..................................................................................... 6
...................................................................................
11.
4. Scope of the Research .........................................................................................
............................................................................................ 8
1.4.
Organization
..................................................................................
1.5 Orga
1.5.
nizati
i tion of
of the
the Thesis ...
........................................................................................... 9
CHAP
APTE
TER
R 2 LITERATURE
LI
REV
EVIEW ................................................................... 10
CHAPTER
REVIEW
2.1 Artificial Neural Network ..................................................................................
................................................................................. 10
researches
..........................................................................
2.2 Review of previous researche
hes ........
................................................................... 11
2.3 Learning Paradigms in ANNs ....
........................................................................ 14
...........................................................................
CHAPTER 3 RESEARCH METHODOLOGY ...................................................... 20
3.1 Statistical Performance Evaluation of the Model.............................................. 22
v
3.2 Financial Performance Evaluation of the Model .............................................. 24
3.3 Research Data................................................................................................... 25
3.4 Data preparation ...
............................................................................................... 26
...............................................................................................
Calculation.........................................................................................
3.5 Variablee C
alculation.......................................................................................... 27
CHAPTER
DESCRIPTIVE
CHAP
PTER 4 DES
SCR
CRIP
IPT
TIVE STATISTICS
STA
TATI
TIST
STIC
CS ..........................................................
...................................................................... 31
CHAPTER
RESEARCH
CH
CHAPTE
ER 5 R
ESEARC
RCH
H RESULTS AND ANALYSIS
AN
NAL
ALYSIS .....................................
.......................................... 36
5.1 Comparison
Com
ompariso
son of Financial Performance..............................................................
Performance..................................................................... 36
.............................................................
5.2 Comparison
5.2
Com
mparison of Statistical Performance ...................................
................................ 45
CHAPTER
CH
C
AP
PTER 6 CONCLUSION ..................................................................................
...................................................................................... 49
REFERENCES
REFE
ERENCES .........................................................................................................
............................................................................................................ 54
Apendix
code...........................................................................................
Ap
Apen
endix A: Matlab code.......................................................................
......................... 58
...................................................................................................
A. Preprocess code ..........................
............................................................................... 58
Training
.......................................................................................................
B. Tr
B
Trai
aini
ning
ng code ............................................................................
.................................... 60
C. Testing
Tes
esting cod
ode.
e........................................................................................................................... 73
code.........................................................................................................
vi
List of Tables
Table 1. The number off ssample
ample in the entire data
ta sset
et ............................................... 26
Selected
indicators
........................................
Table 2. Selec
cte
ted
d technical indi
d cators and their formulas ......
.................................... 28
Variables
.......................................................................................
Table 3. Defined V
aria
ar
iabl
bles
e .........................
............
.............................................................. 30
ANN
parameter
.....................................
Ta 4. A
Table
NN pparameter
arametter llevels
evels tested in paramete
terr setting ........
.................................... 32
Tabl
blee 5.
5 S
umm
mary statistics for the selected indicators ..........
......................................... 33
Table
Summary
............................................
Ta
Tabl
b e 6. T
hree parameters for training and testing of ANN mode
el ......
............................ 37
Table
Three
model
..........................
Ta
T
ble 77:: Testing with parameter combination (10, 0.2 , 0.5, 1e6) ...................
............. 38
Table
............................
Tablee 8. Testing with parameter combination (30, 0.3, 0.5, 1e6) ......................
.......... 39
.............................
Ta
Tabl
b e: 9. Testing with pparameter
arameter combination ((50,
50,, 0.2, 0.5, 1e-6) ......
........................ 39
Table:
..........................
Table 10. Summary of the best fore
reca
c st
stin
ing, parameters (10, 0.2 , 0.5, 1e6) ...
............. 41
forecasting,
............
Ta
Tabl
blee 11
11. Financial pe
pperformance
rformance of ANN model ...........................
................................... 42
Table
.....................................................
Ta
Tabl
blee 12. The em
empi
pirica
i al re
resu
sult
lt of ot
othe
herr rresearch
esearrch .....
......................................................... 44
Table
empirical
result
other
......................................................
Table: 13 the best statistic & financial
fina
nancial performance
peerf
r ormance ............................................... 46
Table 14. Statistical performanc
ce of ANN m
odel .................................................... 48
performance
model
vii
List of Figures
Fig. 1 An artificial neural
neurral network is an interconnected
interconn
nnected group of nodes................. 11
Fig. 2 A Neura
Neural
..........................................
rall network with
h three-layer feed forward ........
.................................... 16
Fig. 3 Tan-Sigmoid
Function
Linear
Transfer
Function
Tan-Sigmoid
id Transfer
Tra
ran
nsfer Fu
Func
ncti
tion
on and
nd Li
Line
near
ar T
r nsfer Func
ra
cti
tion ................. 31
Fig.
Data
preparation
normalized
technical
Fi 4 Dat
ataa pr
prep
eparation
n (actual
(a
technical parameters
parameete
ters
r & nor
rma
mali
lize
zed techni
nical
parameters)
......................................................................................................
para
pa
ram
meteers
rs) ..................................................................
......................................... 34
Fig.
...................................................................
Fig
Fi
g. 5 Training
Tra
raining process of ANN model ........................................
............................... 34
Fig.
..................................................................................
Fi 6 Testing of ANN model ............................................................
........................... 35
Fig.77 Predict next trading day, by entering new data to the network ................
......................
........ 35
Fig.
Fig 8 Training & Forecasting performance (%) of ANN model for a whole
whoole data
datta
set (n = 50
..........................................................
50, η = 00.2,
.2
2, μ = 00.5,
.5,, ep = 1e6).
.5
1e6
e6)). ..................................................
............ 41
..............
Fig. 9 Forecasting performance (%) of ANN model for various η values
Fig.
es ...
.............. 43
viii
ABBREVATIONS
GDP : gross domestic product
prodduc
uctt
IA
: artificial
artificiaal iintelligent
ntelligent
ANN : artificial neur
u al nnetwork
etwork
k
neural
ID
DX
IDX
In
ndo
done
nesi
sian Sto
tock
ck Index
: Indonesian
Stock
JKSE
E : Jakarta
Jakar
artta Stock Exchange (Pervious name of IDX)
MAE : m
MAE
ean absolute error
mean
RMSE
E : root mean square error
MA E : mean absolute percentage error
MAPE
R2
: goodness of fit
APE
APE
abso
solu
lute
te ppercentage
erce
er
cent
ntag
agee er
erro
r r
: ab
absolute
error
PO
O
pre
redi
dict
cted
ed ooutput
utp
ut
put
: predicted
AO
: actual output
CCI
x
: commodity channel index
MACD: moving average convergencee divergence
divergence
ROC : price-rate-of change
RSI
: relative strength index
ix
PR
: predicted rate (forecasting rate)
n
: neuron
η
: learning rate
μ
constant
: momentum co
ep
: epoch
h
IT
informationn technology
tec
echn
hnologyy
: information
LSM
M : The Li
Liby
byan Exchange
Exchang
ngee St
Stockk Ma
rkkett
Libyan
Market
T
EPIX : The
Thee Tehran
T hran
Te
an Exchange Price Index
TEPIX
x