6
Technical analysis assumes that the stock market moves in trends and these trends can be captured and used for forecasting. Technical analysis belongs to
the third school of thought. It attempts to use past stock price and volume information to predict future price movements The technical analyst believes
that there are recurring patterns in the market behavior that are predictable. In fact, there is not any research proved the existing of such patterns due to each
stock market has different characteristics, depending on the economies they are related to, and varying from time to time. Most of the techniques used by
technical analysts have not been shown to be statistically valid and many lack a rational explanation for their use. However, technical analysis has its value
on forecasting. Artificial Neural Networks are regarded by many as one of the more suitable
techniques for stock market forecasting Yao Tan 2001. It has been demonstrated to be an effective technique for capturing dynamic non-linear
relationships in stock markets, while technical analysis techniques unable to do so.
1.2. Objective of the Research
The core objective of this research is to forecast the direction of movement in the daily JKSE or IDX using artificial neural network, also to compare a
financial performance model and statistical model of ANN on its precision of stock price prediction. At the same times, its empirical result will be
comparing with some recently researcher’s works in this market that used ANN models. Also, this research will seek to prove against a validity of the
the third school of though gh
t t.
I It attempts
t t
t o
o use past stock price and volume
information to p p
r redict future price movements The
e te
te chnical analyst believes
that there re
are recurring patte e
rn n
s s
in in
t t
he he
m m
arke ke
t behavior that t
ar a
e predictable. In fact
ct , there is not
a a
ny ny
r r
es e
earch proved the e i
xi st
stin in
g g of
of such patterns ns
due to each stock ma
mark rket
et has d
d if
if fe
fe re
nt charact
er istics, de
pe e
nd nd
ing on on
t t
he he
economi mies they
ar e
e re
re la
lat ted to
to ,
a nd varying from ti
me to time. Mo
st of
th the tech
ch ni
niqu ques
es use
d d
by te
h chnica
a l
analysts hav e
not been s ho
wn to be s ta
ti stically v
al l
id id
and d m
m an
y lack ck
a a rati
ti on
al explanation for their u
se .
Howe ve
r, technical analy si
i s
s has s
it it
s s
va v
lue on f
f orecasting.
Ar r
tificial Neural Networks are regarded by
m any as one of the
m more s
s u
uitabl l
e tech
ni qu
es s
f f
or or
s s
to to
ck ck
m m
ar ar
ket t
fo f
recast t
in ing
g Y
Y ao
ao T
T an
an 2
2 00
1 . It has be
be en
en demonstrated to be an effective
te technique for capturing dynamic non
n-li line
ne a
ar re
re la
l ti
i on
n sh
sh ip
ip s
s in
in s
s to
to ck
ck m
m ar
ar ke
ke ts, while tech
ch ni
ni ca
ca l
l an
an al
al ys
ys is
is t
t ec
ec hn
hn i
iques s
un unab
able e to
do do s
s o.
1.2. Objective of the Research
The core objective of this res s
earch is to o forecast the direction of movement in
the daily JKSE or IDX using g
artific cial neural network, also to compare a
financial performance model and st
statistical model of ANN on its precision of
7
Efficient Market Hypothesis and the Random Walk Hypothesis for short-term trading advantages in this stock market, which is considered as one of the
most important emerging markets in Asia.
1.3. Contribution of the Research
