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J
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Vo
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39
,
No
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1
,
J
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20
25
:
575
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5
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in
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f
r
o
m
v
ar
io
u
s
s
o
u
r
ce
s
,
s
u
ch
as f
o
r
u
m
d
is
cu
s
s
io
n
s
,
R
SS
f
ee
d
s
,
T
witter
twee
ts
,
an
d
n
ews
p
o
r
t
als.
T
ec
h
n
ical
d
ata
is
d
ir
ec
tly
co
llected
f
r
o
m
Y
ah
o
o
Fin
an
ce
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
Pre
d
ictin
g
s
to
ck
p
r
ices
ca
n
b
e
ch
allen
g
in
g
d
u
e
to
f
ac
t
o
r
s
lik
e
th
e
ec
o
n
o
m
y
,
in
v
esto
r
s
en
tim
en
t,
an
d
p
o
liti
ca
l
d
ev
elo
p
m
e
n
ts
.
T
h
e
ef
f
icien
t
m
ar
k
et
h
y
p
o
th
esis
claim
s
th
at
p
r
ed
ictin
g
s
to
ck
p
r
ice
s
is
im
p
o
s
s
ib
le,
b
u
t
th
er
e
is
al
way
s
th
e
p
r
esen
ce
o
f
in
ef
f
icien
cy
t
h
at
h
elp
s
to
p
r
ed
ict
s
to
ck
p
r
ices to
g
et
ab
n
o
r
m
a
l r
etu
r
n
s
[
6
]
-
[
8
]
.
Statis
t
ical
tech
n
iq
u
es
ar
e
co
m
m
o
n
ly
u
s
ed
f
o
r
s
to
ck
p
r
ice
p
r
ed
ictio
n
.
T
h
e
m
o
s
t
wid
ely
u
s
ed
tech
n
iq
u
es
in
clu
d
e
ex
p
o
n
e
n
t
ial
s
m
o
o
th
in
g
,
au
to
r
e
g
r
ess
iv
e
in
teg
r
a
ted
m
o
v
i
n
g
av
er
a
g
e
(
AR
I
MA
)
,
an
d
r
eg
r
ess
io
n
m
et
h
o
d
.
Ho
wev
er
,
ML
alg
o
r
ith
m
s
h
av
e
s
ig
n
if
ica
n
tly
im
p
r
o
v
ed
th
e
ac
c
u
r
ac
y
o
f
s
to
ck
m
ar
k
et
p
r
ice
p
r
ed
ictio
n
s
[
2
]
,
[
9
]
,
[
1
0
]
.
Su
p
p
o
r
t
v
ec
t
o
r
r
eg
r
ess
io
n
(
SVM)
is
a
p
o
p
u
lar
m
ac
h
in
e
-
lea
r
n
in
g
tech
n
iq
u
e
f
o
r
p
r
ed
ictin
g
s
to
c
k
p
r
ices.
SVM
is
a
w
id
ely
u
s
ed
ML
tech
n
iq
u
e
to
p
r
ed
ict
s
to
ck
p
r
ices
[
1
1
]
,
[
1
2
]
.
On
e
o
f
th
e
s
tu
d
ies
r
ec
o
m
m
en
d
ed
t
h
at
f
u
tu
r
e
r
esear
c
h
in
clu
d
e
m
o
r
e
tec
h
n
ical
an
aly
s
is
in
d
icato
r
s
an
d
a
lar
g
e
r
g
am
u
t
o
f
s
to
ck
s
an
d
m
ar
k
ets
[
2
].
Oth
er
r
esear
ch
u
s
ed
th
e
s
am
e
a
p
p
r
o
ac
h
to
p
r
e
d
ict
s
to
ck
p
r
ic
e
m
o
v
em
en
ts
[
1
3
]
.
T
h
e
m
o
d
el
p
r
o
p
o
s
ed
b
y
th
e
s
t
u
d
y
u
s
ed
th
e
m
u
ltip
le
r
eg
r
ess
io
n
m
eth
o
d
o
n
2
4
ec
o
n
o
m
ic
in
d
icato
r
s
.
So
m
e
o
f
th
e
r
esear
ch
er
s
h
ad
p
r
esen
ted
a
n
o
v
el
d
ataset
th
a
t
co
m
b
in
es
tech
n
ical
s
to
ck
m
ar
k
et
d
ata
w
ith
n
ews
s
en
tim
en
t
a
n
d
k
ey
ec
o
n
o
m
ic
in
d
icato
r
s
s
u
ch
as
i
n
f
latio
n
,
GDP,
ex
ch
a
n
g
e
r
ates,
an
d
in
ter
est
r
ates
o
r
in
teg
r
ates
tech
n
ical
in
d
ica
to
r
s
,
co
n
tex
tu
al
in
f
o
r
m
atio
n
,
an
d
f
in
a
n
cial
d
ata,
em
p
lo
y
in
g
a
h
eu
r
is
tic
s
to
ck
s
elec
tio
n
alg
o
r
ith
m
to
f
in
d
s
to
ck
s
with
h
i
g
h
p
r
ed
icted
d
aily
r
etu
r
n
s
.
T
h
eir
ev
alu
atio
n
ac
r
o
s
s
s
to
ck
an
d
cr
y
p
to
cu
r
r
en
c
y
m
ar
k
ets
s
h
o
w
s
en
h
an
ce
d
p
er
f
o
r
m
a
n
ce
co
m
p
ar
ed
to
ex
is
tin
g
m
eth
o
d
s
.
T
h
ey
em
p
h
asize
th
e
s
ig
n
if
ican
t
r
o
le
o
f
n
ews
s
en
tim
en
t
o
n
s
to
ck
p
r
ices
an
d
h
ig
h
lig
h
t
th
e
n
ee
d
f
o
r
f
u
r
th
er
r
esear
ch
to
ad
d
r
ess
th
e
co
m
p
lex
ities
o
f
n
o
n
-
lin
ea
r
an
d
n
o
n
-
s
tatio
n
ar
y
d
ata
in
s
to
ck
m
ar
k
et
p
r
e
d
ictio
n
s
[
1
4
]
,
[
1
5
]
.
A
r
e
v
i
e
w
o
f
a
r
t
i
c
le
s
o
n
M
L
in
s
t
o
c
k
p
r
i
c
e
f
o
r
e
c
as
ti
n
g
f
o
u
n
d
t
h
a
t
m
a
n
y
s
t
u
d
i
e
s
f
a
v
o
r
e
d
s
h
o
r
t
e
r
t
i
m
e
f
r
a
m
e
s
.
R
a
n
d
o
m
f
o
r
es
t
(
R
F
)
,
S
V
M
,
a
r
t
i
f
i
c
i
al
n
e
u
r
a
l
n
e
t
w
o
r
k
(
A
N
N
)
,
d
e
c
is
i
o
n
t
r
e
e
(
D
T
)
,
l
o
g
i
s
t
i
cs
r
e
g
r
es
s
i
o
n
(
L
R
)
,
a
n
d
K
-
n
e
a
r
es
t
n
e
i
g
h
b
o
r
(
K
N
N
)
w
e
r
e
t
h
e
m
o
s
t
c
o
m
m
o
n
ly
u
s
e
d
a
l
g
o
r
i
t
h
m
s
[
4
]
,
[
1
6
]
,
[
1
7
]
.
R
esear
ch
g
ap
id
en
tific
atio
n
an
d
p
r
o
p
o
s
ed
m
o
d
el
:
R
esear
ch
er
s
h
av
e
u
s
ed
ML
alg
o
r
ith
m
s
lik
e
SVM
an
d
R
F
to
f
o
r
ec
ast
s
to
ck
p
r
ic
es.
R
esear
ch
er
s
also
u
s
ed
s
en
tim
en
t
an
aly
s
is
b
ased
o
n
s
o
ci
al
m
ed
ia
o
r
n
ews
f
ee
d
s
to
i
d
en
tify
ex
p
r
ess
ed
s
en
tim
en
t
to
war
d
s
p
ec
if
ic
s
to
ck
s
f
o
r
f
o
r
ec
asti
n
g
.
T
h
e
s
tu
d
y
b
y
Ma
in
i
an
d
Go
v
in
d
a
[
1
8
]
f
o
u
n
d
th
at
R
F p
er
f
o
r
m
e
d
b
etter
th
an
SVM.
S
en
tim
e
n
t a
n
aly
s
is
ca
n
also
b
e
u
s
ed
to
s
u
g
g
est im
p
r
o
v
em
en
ts
f
o
r
p
r
o
d
u
cts
an
d
s
er
v
ices
[
1
9
]
.
N
aïv
e
B
ay
es
clas
s
if
ier
ca
n
h
elp
to
class
if
y
p
ess
im
i
s
tic
an
d
o
p
tim
is
tic
s
ets
b
ased
o
n
th
e
u
n
d
er
ly
in
g
s
en
tim
en
t f
o
r
s
to
ck
p
r
ice
p
r
ed
ictio
n
[
2
0
]
.
A
co
m
p
r
eh
e
n
s
iv
e
liter
atu
r
e
r
e
v
iew
o
f
s
en
tim
en
t
an
aly
s
is
an
d
ML
alg
o
r
ith
m
s
h
as
p
r
o
v
i
d
ed
in
s
ig
h
t
an
d
id
e
n
tifie
d
r
esear
ch
g
ap
s
[
2
1
]
-
[
2
3
]
.
T
h
e
r
ev
iew
f
o
u
n
d
t
h
at
m
o
s
t
in
ter
n
atio
n
al
s
tu
d
ies
d
id
n
o
t
in
clu
d
e
th
e
I
n
d
ian
m
a
r
k
et
as sam
p
le
d
ata.
T
h
e
g
eo
g
r
ap
h
ical
in
clu
s
io
n
o
f
th
e
wo
r
ld
’
s
s
ec
o
n
d
-
l
a
r
g
est
m
ar
k
et
will
cr
ea
te
a
s
ig
n
if
ican
t
g
ap
,
as
I
n
d
ia
is
am
o
n
g
th
e
to
p
ten
lar
g
est
s
to
ck
m
ar
k
ets
g
lo
b
ally
[
2
4
]
-
[
2
6
]
.
Ad
d
itio
n
ally
,
f
ew
s
tu
d
ies
co
v
er
ed
2
0
2
0
,
wh
ich
was a
f
f
ec
ted
b
y
th
e
C
OVI
D
-
1
9
p
an
d
em
ic
[
2
7
]
-
[
3
0
]
.
T
h
e
r
ev
iew
also
id
en
tifie
d
a
n
ee
d
f
o
r
m
o
r
e
d
iv
e
r
s
e
d
ata
s
o
u
r
ce
s
,
in
clu
d
in
g
s
o
cia
l
m
ed
ia
p
latf
o
r
m
s
an
d
tech
n
ical
in
d
icato
r
s
a
f
ter
a
n
y
cr
is
is
p
er
io
d
,
s
u
ch
as
C
OVI
D
-
1
9
[
5
]
,
[
9
]
,
[
1
0
]
,
[
3
1
]
.
An
o
th
er
s
tu
d
y
s
elec
ted
ten
r
ev
iews,
in
clu
d
in
g
o
v
er
3
7
9
s
tu
d
ies,
b
ased
o
n
s
cr
ee
n
in
g
6
9
titl
es.
T
h
ey
em
p
h
asize
th
e
u
s
e
o
f
S
VM
,
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
,
an
d
n
e
u
r
al
n
etwo
r
k
s
(
NN)
[
3
2
]
.
On
e
ca
n
also
u
s
e
ANN,
S
VM
,
an
d
L
STM
n
etwo
r
k
s
to
p
r
ed
ict
s
to
ck
m
ar
k
et
tr
en
d
s
[
3
3
]
.
His
to
r
ical
s
to
ck
p
r
ices
wer
e
th
e
p
r
im
a
r
y
d
ata
s
o
u
r
ce
,
with
ac
cu
r
ac
y
b
ein
g
th
e
k
e
y
p
e
r
f
o
r
m
an
ce
m
etr
ic.
T
h
e
cu
r
r
en
t
s
tu
d
y
also
f
o
c
u
s
ed
o
n
h
is
to
r
ical
p
r
ices
f
o
r
p
r
ed
ictio
n
m
o
d
elin
g
an
d
ac
cu
r
ac
y
as
a
co
m
p
ar
is
o
n
m
atr
ix
to
em
p
h
a
s
ize
th
e
im
p
o
r
tan
ce
o
f
th
e
h
y
b
r
i
d
m
o
d
el
in
s
to
ck
m
ar
k
et
p
r
e
d
ictio
n
[
3
2
]
,
[
3
3
]
.
Var
io
u
s
r
esear
ch
er
s
h
av
e
s
h
o
wn
th
at
SVM,
R
F,
an
d
L
R
p
r
o
v
id
e
b
etter
ac
cu
r
ac
y
d
u
r
in
g
C
OVI
D
-
19
an
d
o
th
er
cr
is
es,
aid
in
g
in
cr
e
atin
g
ea
r
ly
war
n
in
g
s
ig
n
als
to
p
r
ev
en
t
wea
lth
d
estru
ctio
n
.
T
h
ey
em
p
h
asize
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
P
r
ed
ictive
mo
d
elin
g
fo
r
eq
u
ity
tr
a
d
in
g
u
s
in
g
s
en
timen
t
a
n
a
ly
s
is
(
Go
n
d
a
liya
C
h
eta
n
)
577
im
p
o
r
tan
ce
o
f
h
y
b
r
i
d
m
o
d
els
o
v
er
s
in
g
le
p
r
ed
ictio
n
m
eth
o
d
o
lo
g
ies
to
b
en
ef
it
p
o
licy
m
a
k
e
r
s
,
in
v
est
o
r
s
,
an
d
co
r
p
o
r
atio
n
s
[
3
4
]
-
[
3
6
]
.
I
n
th
e
o
th
er
f
in
d
in
g
s
,
it
was
e
v
id
en
t
th
at
in
cr
ea
s
in
g
th
e
u
s
e
o
f
h
y
b
r
id
ap
p
r
o
ac
h
es
t
h
at
co
m
b
in
e
M
L
an
d
s
tatis
tical
m
eth
o
d
s
en
h
a
n
ce
s
p
r
ed
ictio
n
ac
cu
r
ac
y
.
A
u
th
o
r
s
r
ec
o
m
m
en
d
ad
o
p
tin
g
PR
I
SMA
2
0
2
0
s
o
r
esear
ch
er
s
,
ed
ito
r
s
,
an
d
r
ev
i
ewe
r
s
ca
n
ac
h
iev
e
m
o
r
e
ac
c
u
r
ate
r
ep
o
r
tin
g
in
t
h
eir
wo
r
k
[
3
7
]
-
[
4
0
]
.
Hen
ce
,
th
e
cu
r
r
e
n
t stu
d
y
d
o
es n
o
t in
cl
u
d
e
o
n
l
y
s
tatis
tics
b
u
t te
x
t m
in
in
g
as a
to
o
l in
a
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el.
R
ev
iew
r
esear
ch
p
ap
er
with
r
ev
iew
o
f
5
7
r
esear
ch
ar
ticles o
n
p
r
ed
ictin
g
s
to
ck
m
ar
k
et
b
eh
a
v
io
r
u
s
in
g
tim
e
s
e
r
ies
an
aly
s
i
s
,
tex
t
m
in
i
n
g
,
an
d
s
en
tim
en
t
an
aly
s
is
th
at
o
u
tlin
e
th
e
ch
allen
g
es
an
d
tech
n
iq
u
es
ass
o
ciate
d
with
ea
ch
m
eth
o
d
an
d
em
p
h
a
s
ize
s
th
e
b
en
ef
its
o
f
h
y
b
r
id
m
o
d
els
f
o
r
im
p
r
o
v
ed
ac
cu
r
ac
y
[
4
1
]
.
T
h
e
r
e
v
iew
h
ig
h
lig
h
ts
th
e
in
cr
ea
s
in
g
tr
en
d
o
f
in
teg
r
atin
g
d
iv
er
s
e
s
o
u
r
ce
s
,
lik
e
s
o
cial
m
e
d
ia
an
d
n
e
ws,
in
to
p
r
ed
ictio
n
m
o
d
els
,
b
u
t
also
n
o
tes
lim
itatio
n
s
s
u
ch
as
m
an
ag
in
g
l
ar
g
e
d
ata
v
o
lu
m
es
an
d
ac
c
u
r
ac
y
in
s
en
tim
e
n
t
class
if
icatio
n
[
4
1
]
.
T
h
e
o
th
er
r
esear
ch
er
also
s
u
g
g
ested
f
ea
tu
r
e
-
b
ased
f
o
r
ec
ast
m
o
d
el
av
er
ag
in
g
(
FF
OR
MA
)
,
an
au
to
m
ated
m
eth
o
d
f
o
r
weig
h
ted
f
o
r
ec
ast
co
m
b
i
n
atio
n
s
b
ased
o
n
tim
e
s
er
ies
f
ea
tu
r
es.
Ho
wev
er
,
it
also
r
eq
u
ir
es
co
n
s
id
er
atio
n
o
f
tr
a
n
s
ac
tio
n
co
s
ts
an
d
r
is
k
s
tr
ateg
ies,
ca
llin
g
f
o
r
f
u
r
th
er
r
esea
r
ch
to
im
p
r
o
v
e
its
ad
ap
tab
ilit
y
an
d
r
esp
o
n
s
iv
e
n
e
s
s
to
m
ar
k
et
ch
an
g
es
[
4
2
]
-
[
4
6
]
.
Alo
n
g
th
e
s
am
e
lin
es,
th
e
cu
r
r
en
t
s
tu
d
y
also
u
s
ed
m
u
ltip
le
s
o
cial
m
ed
ia
s
o
u
r
ce
s
to
cr
ea
te
a
h
y
b
r
i
d
m
o
d
el
f
o
r
p
r
ed
ictin
g
s
to
c
k
p
r
ices.
Similar
ly
,
s
en
tim
en
t
an
aly
s
is
r
esear
ch
f
o
cu
s
ed
o
n
lim
ited
p
ar
am
eter
s
an
d
d
id
n
o
t
u
s
e
h
y
b
r
id
o
r
en
s
em
b
le
tech
n
i
q
u
es
[
4
7
]
-
[
4
9
]
.
T
h
e
r
e
v
iew
s
u
g
g
ests
th
at
b
etter
f
ea
tu
r
e
s
elec
tio
n
tec
h
n
iq
u
es
co
u
ld
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
o
f
s
to
ck
p
r
ice
p
r
e
d
ictio
n
s
[
4
8
]
-
[
5
1
]
.
Fu
r
th
er
m
o
r
e
,
v
er
y
f
ew
s
tu
d
i
es
ap
p
lied
m
u
ltip
le
m
eth
o
d
s
to
ag
g
r
eg
ate
r
esu
lts
[
5
2
]
,
[
5
3
]
.
B
y
co
m
b
in
in
g
th
ese
f
ac
to
r
s
,
th
e
p
r
o
p
o
s
ed
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
tem
aim
s
to
im
p
r
o
v
e
th
e
ac
c
u
r
ac
y
an
d
p
r
ec
is
io
n
o
f
s
to
ck
m
ar
k
et
p
r
e
d
ictio
n
s
f
o
r
s
h
o
r
t
-
ter
m
d
u
r
atio
n
s
.
Fig
u
r
e
1
s
h
o
ws
th
e
p
r
o
p
o
s
ed
f
lo
w
ch
ar
t
b
ased
o
n
t
h
e
r
esear
ch
g
ap
i
d
en
tifie
d
in
th
e
l
iter
atu
r
e
to
ad
d
r
ess
th
e
p
u
r
p
o
s
e
o
f
th
e
s
tu
d
y
.
3.
M
E
T
H
O
D
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
a
m
o
d
el
to
p
r
e
d
ict
s
h
o
r
t
-
te
r
m
s
to
c
k
p
r
ices
u
s
in
g
in
v
esto
r
s
en
t
im
en
t
an
d
tech
n
ical
in
d
icato
r
s
.
I
t
aim
s
t
o
an
aly
ze
th
e
im
p
ac
t
o
f
in
v
es
to
r
s
en
tim
en
t
an
d
m
icr
o
-
b
lo
g
g
in
g
o
n
s
to
ck
p
r
ices
an
d
i
d
en
tify
u
s
ef
u
l
tech
n
ical
in
d
icato
r
s
f
o
r
t
r
en
d
id
en
tific
at
io
n
.
T
h
e
m
ain
r
esear
ch
o
b
ject
iv
es
ar
e
t
o
r
e
v
iew
s
en
tim
en
t
d
ata
co
llectio
n
s
o
u
r
ce
s
,
co
m
p
ar
e
tex
t
-
m
in
in
g
a
n
d
d
ata
-
m
in
in
g
tech
n
iq
u
es,
p
r
o
p
o
s
e
a
n
ew
m
eth
o
d
to
m
ea
s
u
r
e
th
e
im
p
ac
t
o
f
n
e
ws
an
d
in
v
esto
r
s
en
tim
en
t,
an
aly
ze
tech
n
ical
i
n
d
icato
r
s
,
c
o
m
p
ar
e
d
if
f
er
e
n
t
ML
tech
n
iq
u
es,
an
d
ev
alu
ate
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
’
s
p
r
ec
is
io
n
,
r
ec
all,
s
p
ec
if
icity
,
s
u
b
jectiv
ity
,
an
d
ac
cu
r
ac
y
.
3
.
1
.
Da
t
a
a
nd
s
ec
t
o
r
s
elec
t
ed
T
h
e
p
r
o
p
o
s
ed
r
esear
ch
will
b
e
co
n
d
u
cted
in
th
e
b
a
n
k
in
g
,
p
h
ar
m
a,
an
d
r
ea
l
estate
s
ec
to
r
s
.
T
h
ese
s
ec
to
r
s
ar
e
co
r
e,
l
o
n
g
-
last
in
g
,
an
d
n
e
v
er
-
en
d
in
g
in
I
n
d
ia,
r
e
p
r
esen
tin
g
alm
o
s
t
8
0
%
o
f
th
e
e
co
n
o
m
y
.
T
h
e
y
ar
e
also
th
e
f
astes
t
-
g
r
o
win
g
s
ec
to
r
s
.
B
an
k
,
Ph
ar
m
a,
an
d
R
ea
l E
s
tate
s
ec
to
r
s
to
ck
s
ac
co
u
n
t f
o
r
a
s
ig
n
if
ican
t p
o
r
tio
n
o
f
m
u
tu
al
f
u
n
d
h
o
l
d
i
n
g
s
in
m
o
s
t
m
u
tu
al
f
u
n
d
s
ch
em
es
,
co
v
er
in
g
a
p
p
r
o
x
im
ately
1
0
-
1
5
%
o
f
h
o
ld
i
n
g
s
d
ep
en
d
i
n
g
o
n
th
e
m
u
tu
al
f
u
n
d
’
s
o
b
jectiv
e
an
d
s
ch
em
e.
Ad
d
itio
n
ally
,
th
ese
s
ec
to
r
s
ar
e
lis
ted
in
th
e
to
p
ten
s
ig
n
if
ican
t
allo
ca
tio
n
s
in
th
e
Un
io
n
B
u
d
g
et
allo
ca
tio
n
o
f
th
e
last
th
r
ee
y
ea
r
s
.
Fo
r
ex
a
m
p
le,
i
n
th
e
Un
io
n
B
u
d
g
et
2
0
2
1
,
th
e
Min
is
tr
y
o
f
Hea
lth
an
d
Fam
ily
W
elf
ar
e
h
as
allo
ca
ted
1
0
.
3
5
b
illi
o
n
US
d
o
llar
s
,
wh
ile
t
h
e
Min
is
tr
y
o
f
Ho
u
s
in
g
an
d
Ur
b
an
Af
f
air
s
h
as
allo
ca
ted
7
.
6
4
b
illi
o
n
US
d
o
ll
ar
s
.
T
h
e
b
an
k
in
g
s
ec
to
r
r
e
p
r
es
en
ts
th
e
c
o
u
n
tr
y
’
s
o
v
er
all
ec
o
n
o
m
y
a
n
d
is
a
co
r
e
s
ec
to
r
th
at
in
v
o
lv
es
m
o
s
t
o
f
th
e
co
u
n
tr
y
’
s
s
ec
to
r
d
e
v
elo
p
m
en
t.
T
h
e
r
ef
o
r
e,
th
e
r
esear
ch
er
h
as
s
elec
ted
t
h
e
b
an
k
in
g
,
Ph
ar
m
a,
an
d
r
ea
l e
s
tate
s
ec
to
r
s
f
o
r
th
e
p
r
o
p
o
s
ed
r
es
ea
r
ch
wo
r
k
ex
p
e
r
im
en
ts
.
T
h
r
ee
s
to
ck
s
ar
e
s
elec
ted
f
o
r
t
h
e
f
in
al
e
x
p
er
im
e
n
t
p
r
o
ce
s
s
in
ea
ch
s
ec
to
r
.
T
h
e
s
to
ck
s
elec
tio
n
p
r
o
ce
s
s
in
ea
ch
in
d
u
s
tr
y
is
d
ec
i
d
ed
b
y
u
s
in
g
th
ese
th
r
ee
f
ac
to
r
s
:
th
e
m
o
s
t
ex
te
n
s
iv
e
ca
p
italizatio
n
s
to
ck
in
th
e
in
d
u
s
tr
y
,
ce
n
tr
al
h
o
ld
in
g
in
th
e
Sen
s
ex
an
d
Nif
ty
in
d
e
x
es
a
n
d
Hig
h
liq
u
id
i
ty
.
T
h
ese
p
a
r
a
m
eter
s
ar
e
u
s
ed
to
s
elec
t
th
e
th
r
ee
d
if
f
er
en
t
s
to
ck
s
in
ea
ch
s
ec
to
r
f
o
r
th
e
ex
p
e
r
i
m
en
tal
p
r
o
ce
s
s
o
f
th
e
p
r
o
p
o
s
e
d
m
o
d
el.
Mo
r
eo
v
er
,
th
e
s
to
ck
s
elec
tio
n
p
r
o
ce
s
s
also
co
n
s
id
er
s
s
m
all
-
ca
p
,
m
i
d
-
ca
p
,
an
d
la
r
g
e
-
ca
p
f
ac
to
r
s
.
T
h
er
ef
o
r
e,
T
o
r
r
e
n
t
Ph
ar
m
a
c
eu
ticals
(
T
OR
R
E
NT
PHA
R
M
)
,
Su
n
Ph
ar
m
ac
eu
tical
I
n
d
u
s
tr
ies
L
td
(
SUNPHA
R
MA
)
,
an
d
B
io
co
n
(
B
I
OC
ON)
s
to
ck
s
ar
e
s
elec
te
d
f
o
r
th
e
ex
p
er
im
en
t
in
th
e
p
h
ar
m
a
s
ec
to
r
.
I
n
th
e
b
an
k
s
ec
to
r
,
th
e
State
B
an
k
o
f
I
n
d
ia
(
SB
I
)
,
HDFC
B
an
k
(
HDFC
B
ANK)
,
an
d
AXI
S
B
an
k
(
AXI
SB
ANK)
s
to
c
k
s
ar
e
s
elec
ted
f
o
r
th
e
e
x
p
er
im
en
t.
Go
d
r
ej
Pr
o
p
er
ties
L
im
ited
(
GODREJP
R
OP)
,
D
L
F
L
td
(
DL
F),
a
n
d
Ho
u
s
in
g
an
d
Ur
b
an
Dev
el
o
p
m
en
t
C
o
r
p
o
r
atio
n
(
HUDCO)
s
to
ck
ar
e
s
elec
ted
f
o
r
th
e
p
r
o
p
o
s
ed
m
o
d
el
r
esear
c
h
ex
p
er
im
en
t
p
r
o
ce
s
s
in
th
e
r
ea
l
estate
s
ec
to
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
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I
n
d
o
n
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J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
39
,
No
.
1
,
J
u
ly
20
25
:
575
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5
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3
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2
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u
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r
o
p
o
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f
lo
w
ch
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ased
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th
e
r
es
ea
r
ch
g
ap
i
d
en
tifie
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in
th
e
liter
atu
r
e
to
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s
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s
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en
b
elo
w.
T
h
e
Ps
eu
d
o
co
d
e
f
o
r
th
e
s
en
s
itiv
ity
s
co
r
e
p
r
o
ce
s
s
:
i)
.
Star
t
.
ii)
.
T
o
k
e
n
ize
th
e
u
s
er
’
s
v
iew
(
n
ew
s
,
d
o
c
u
m
en
t)
in
to
a
W
o
r
d
v
ec
to
r
.
iii)
.
Pre
p
ar
e
th
e
p
o
s
itiv
e,
n
eg
ativ
e
an
d
n
eu
tr
al
d
ictio
n
ar
y
co
n
tai
n
in
g
wo
r
d
s
(
t
o
k
en
s
)
with
th
eir
ca
teg
o
r
y
r
an
k
.
iv
)
.
C
alcu
late
th
e
weig
h
ted
av
e
r
ag
e
o
f
al
l
th
e
to
k
en
s
in
ea
ch
d
ictio
n
ar
y
.
v)
.
C
h
ec
k
a
g
ain
s
t
ea
ch
wo
r
d
o
f
th
e
u
s
er
’
s
v
iew
to
s
ee
wh
eth
er
it
m
atch
es
o
n
e
o
f
t
h
e
wo
r
d
s
in
th
e
d
ictio
n
ar
ies
.
v
i)
.
C
o
u
n
t
th
e
n
u
m
b
er
o
f
m
atch
ed
wo
r
d
s
o
cc
u
r
r
e
n
ce
with
all
th
e
to
k
en
s
o
f
th
e
d
icti
o
n
ar
i
es
.
v
ii)
.
C
alcu
late
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e
s
e
n
s
it
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ity
s
co
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e
o
f
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ch
u
s
er
’
s
v
iew
.
v
iii)
.
C
lass
if
y
th
e
n
ews b
ased
o
n
s
en
s
itiv
ity
s
co
r
e
.
ix
)
.
C
alcu
late
th
e
im
p
ac
t o
f
n
ews
.
x)
.
Sto
p
.
3
.
2
.
2
.
T
ec
hn
ica
l
a
na
ly
s
is
ph
a
s
e
T
ec
h
n
ical
an
aly
s
is
is
a
r
e
s
ea
r
ch
tech
n
iq
u
e
th
at
u
s
es
h
is
to
r
ical
s
to
ck
p
r
ice
d
ata
to
id
en
tify
tr
ad
in
g
o
p
p
o
r
tu
n
ities
.
T
h
is
in
v
o
lv
es
u
n
d
er
s
tan
d
i
n
g
th
e
tr
en
d
s
an
d
p
atter
n
s
o
f
th
e
s
to
ck
m
ar
k
e
t,
wh
ich
r
esear
ch
an
aly
s
ts
ca
n
lo
ca
te.
Ho
wev
er
,
co
m
p
u
tatio
n
al
in
tellig
en
ce
co
m
p
u
tin
g
ap
p
r
o
ac
h
es
h
a
v
e
em
er
g
ed
as
a
m
o
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e
ef
f
icien
t
an
d
u
n
b
iased
alter
n
a
tiv
e
to
tech
n
ical
an
aly
s
is
.
T
ec
h
n
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an
aly
s
is
is
d
iv
id
ed
in
to
two
f
o
r
m
s
:
ch
ar
t
p
atter
n
s
an
d
tech
n
ical
in
d
ica
to
r
s
.
C
h
ar
t
p
atter
n
s
ar
e
s
h
ap
es
th
at
ex
is
t
in
th
e
p
r
ice
ch
ar
t,
an
d
t
ec
h
n
ical
in
d
icato
r
s
an
aly
ze
th
e
s
u
p
p
ly
an
d
d
em
a
n
d
o
f
s
ec
u
r
ities
.
3
.
2
.
3
.
Da
t
a
inte
g
ra
t
io
n
a
nd
no
rm
a
lizing
da
t
a
Data
co
llectio
n
is
th
e
f
ir
s
t
s
tep
in
an
y
r
esear
ch
wo
r
k
.
Op
en
,
h
ig
h
,
lo
w,
clo
s
e
(
OHL
C
)
d
ata
is
u
s
ed
as
th
e
b
ase
p
ar
am
eter
f
o
r
tech
n
ical
s
tu
d
y
.
T
ec
h
n
ical
d
ata
m
ay
co
n
tain
n
o
is
e
an
d
o
u
tlier
s
,
wh
ich
s
h
o
u
l
d
b
e
r
em
o
v
ed
u
s
in
g
p
r
e
-
p
r
o
ce
s
s
in
g
tech
n
i
q
u
es.
Data
p
r
e
-
p
r
o
ce
s
s
in
g
h
elp
s
co
n
v
er
t
r
aw
d
ata
in
to
a
s
tr
u
ctu
r
ed
f
o
r
m
at
th
at
ca
n
b
e
u
s
ed
as
in
p
u
t
f
o
r
th
e
ML
m
o
d
el.
Data
p
r
e
-
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
in
c
lu
d
e
d
ata
clea
n
in
g
,
in
teg
r
atio
n
,
tr
an
s
f
o
r
m
atio
n
,
a
n
d
r
ed
u
ctio
n
.
I
n
th
e
p
r
o
p
o
s
ed
r
esear
ch
s
tu
d
y
,
two
f
ea
tu
r
e
s
ets
ar
e
u
s
ed
,
wh
ich
s
h
o
u
ld
b
e
c
o
m
b
in
e
d
u
s
in
g
d
ata
in
teg
r
atio
n
tec
h
n
iq
u
es.
T
h
e
h
y
p
o
th
esis
o
f
th
e
Stu
d
y
:
Ho
:
I
n
v
esto
r
s
’
s
en
tim
en
ts
an
d
tech
n
ical
in
d
ic
ato
r
s
-
b
ased
p
r
ed
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n
m
o
d
els
d
o
n
o
t
s
ig
n
if
ica
n
tly
im
p
r
o
v
e
p
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ed
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n
ac
c
u
r
ac
y
f
o
r
th
e
s
h
o
r
t
-
ter
m
d
u
r
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n
.
S
t
o
c
k
m
a
r
k
e
t
f
o
r
e
c
as
ti
n
g
h
a
s
b
e
e
n
a
c
h
a
l
l
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n
g
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n
g
t
as
k
d
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e
to
t
h
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n
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m
e
n
t
o
f
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c
h
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l
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,
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h
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m
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c
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m
a
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a
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u
b
je
c
t
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t
e
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p
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ta
t
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.
R
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i
q
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s
t
o
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d
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s
t
h
is
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m
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r
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t
h
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t
h
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p
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p
a
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d
a
c
o
mp
i
l
a
t
i
o
n
o
f
a
c
c
u
r
a
c
y
,
p
r
e
c
i
s
i
o
n
,
r
e
c
a
l
l
,
a
n
d
F
-
mea
s
u
r
e
s.
I
n
b
o
l
d
,
t
h
e
t
o
p
t
h
r
e
e
p
r
e
d
i
c
t
i
o
n
m
e
t
h
o
d
s
a
r
e
b
a
se
d
o
n
p
a
r
t
i
c
u
l
a
r
c
r
i
t
e
r
i
a
.
T
ab
le
2
s
h
o
ws
th
at
th
e
SMS
-
T
M
m
o
d
el
s
ig
n
i
f
ican
tly
im
p
r
o
v
es
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
a
ll
r
ate,
an
d
F
-
m
ea
s
u
r
es.
Hen
ce
,
th
e
s
tu
d
y
r
ejec
ted
th
e
n
u
ll
h
y
p
o
th
esis
.
T
h
is
h
elp
s
tr
a
d
er
s
ac
h
ie
v
e
b
etter
r
etu
r
n
s
in
tr
ad
i
n
g
.
T
h
er
ef
o
r
e,
u
s
in
g
th
e
n
e
wly
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el,
o
n
e
ca
n
tak
e
a
d
v
an
tag
e
o
f
m
ar
k
et
in
ef
f
icien
cy
to
cr
ea
te
ab
n
o
r
m
al
r
etu
r
n
s
in
a
s
h
o
r
t
d
u
r
atio
n
.
T
h
e
f
o
llo
win
g
s
ec
tio
n
p
r
o
v
id
es
a
d
etailed
d
is
cu
s
s
io
n
with
s
p
ec
if
ic
im
p
licatio
n
s
an
d
f
u
tu
r
e
s
co
p
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
P
r
ed
ictive
mo
d
elin
g
fo
r
eq
u
ity
tr
a
d
in
g
u
s
in
g
s
en
timen
t
a
n
a
ly
s
is
(
Go
n
d
a
liya
C
h
eta
n
)
581
T
ab
le
2
.
SMS
-
T
M
p
r
o
p
o
s
ed
m
o
d
el
r
esu
lts
S
c
r
i
p
t
n
a
m
e
A
c
c
u
r
a
c
y
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F
-
mea
su
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e
TO
R
R
EN
TPH
A
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M
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0
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9
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:
t
h
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a
u
t
h
o
r
p
r
e
p
a
r
e
d
5.
DIS
CU
SS
I
O
N,
I
M
P
L
I
C
AT
I
O
N
,
AND
F
URT
H
E
R
SCO
P
E
T
h
e
ef
f
icien
t
m
ar
k
et
h
y
p
o
th
esis
(
E
MH
)
is
th
e
f
o
u
n
d
atio
n
f
o
r
u
n
d
er
s
tan
d
in
g
th
e
s
tate
o
f
t
h
e
m
ar
k
et,
wh
ich
p
o
s
s
ib
ly
h
el
p
s
tr
ad
er
s
c
r
ea
te
h
ig
h
e
r
r
etu
r
n
s
th
an
av
er
ag
e
th
r
o
u
g
h
s
p
ec
if
ic
to
o
ls
a
n
d
tech
n
iq
u
es.
As
p
er
E
MH
tech
n
ical
an
aly
s
is
,
it
d
o
esn
’
t
wo
r
k
wh
en
t
h
e
m
ar
k
et
is
wea
k
,
f
u
n
d
a
m
en
tal
an
al
y
s
is
d
o
es
n
o
t
wo
r
k
wh
e
n
th
e
m
ar
k
et
is
s
em
i
-
s
tr
o
n
g
,
a
n
d
ev
en
i
n
s
id
er
in
f
o
r
m
atio
n
d
o
e
s
n
’
t
wo
r
k
i
n
s
o
lid
f
o
r
m
s
o
f
m
ar
k
ets.
Ho
wev
er
,
to
d
ate,
cr
ea
tin
g
o
r
ac
h
iev
in
g
s
t
r
o
n
g
f
o
r
m
m
a
r
k
ets
h
as
n
o
t
b
ec
o
m
e
p
o
s
s
ib
le
ev
en
in
d
e
v
e
lo
p
ed
co
u
n
tr
ies.
I
n
d
ev
elo
p
in
g
co
u
n
tr
ies
lik
e
I
n
d
i
a,
th
er
e
ar
e
v
as
t
p
o
s
s
ib
ilit
ies
o
f
in
ef
f
icien
c
y
in
tr
ad
in
g
p
lat
f
o
r
m
s
r
esp
o
n
s
e
th
at
m
ay
cr
ea
te
o
p
p
o
r
tu
n
ities
f
o
r
tr
ad
er
s
to
ea
r
n
h
ig
h
e
r
r
etu
r
n
s
th
an
av
er
ag
e
.
Ho
wev
er
,
cr
ea
tin
g
h
ig
h
e
r
r
et
u
r
n
s
r
eq
u
ir
es
u
n
d
e
r
s
tan
d
in
g
lo
ts
o
f
tech
n
ical
an
d
s
en
tim
en
tal
d
ata.
T
o
d
ay
,
b
lo
g
s
an
d
R
S
S
f
ee
d
s
h
av
e
a
lo
t
o
f
p
o
ten
tial
to
i
d
en
tify
in
s
id
er
in
f
o
r
m
atio
n
th
a
t
is
th
e
r
ea
s
o
n
f
o
r
ab
n
o
r
m
al
r
etu
r
n
s
in
s
em
i
-
s
tr
o
n
g
m
a
r
k
ets.
Ag
ain
,
s
eg
r
e
g
atin
g
all
d
ata
an
d
c
o
n
v
er
tin
g
d
at
a
in
to
in
f
o
r
m
atio
n
,
an
d
f
in
ally
,
in
th
e
f
o
r
m
o
f
a
d
ec
is
io
n
to
b
u
y
-
s
ell,
is
ch
alle
n
g
in
g
m
an
u
ally
.
Hen
ce
,
th
e
p
r
o
p
o
s
ed
SMS
-
T
M
m
o
d
el
is
th
e
co
d
in
g
o
f
th
e
wh
o
le
p
r
o
ce
s
s
th
at
d
ir
ec
tly
co
v
er
s
tech
n
ical
an
d
s
en
tim
en
t
d
ata
to
co
n
v
er
t
it
in
to
an
ac
cu
r
ate
p
r
ed
ictio
n
o
f
b
u
y
-
s
ell
ca
lls
f
o
r
tr
ad
er
s
to
cr
ea
te
h
ig
h
er
r
etu
r
n
s
.
T
h
e
cu
r
r
en
t
p
a
p
er
h
as
u
s
e
d
th
e
m
o
s
t
ef
f
icien
t
s
ix
m
eth
o
d
s
f
o
r
cr
ea
tin
g
p
r
ed
ictio
n
m
o
d
el
s
,
n
am
ely
,
KNN,
DT
,
ANN,
SVM,
L
R
,
an
d
R
F
ML
tech
n
iq
u
es
to
p
r
ed
ict
th
e
p
r
icin
g
m
o
v
e
m
en
t
o
f
s
to
ck
s
in
d
if
f
er
en
t
s
ec
to
r
s
wh
er
e
it
was
f
o
u
n
d
th
at
SVM,
L
R
,
an
d
R
F
wer
e
g
iv
in
g
h
ig
h
est
ac
cu
r
ac
y
in
all
th
e
p
r
ed
ictio
n
m
o
d
elin
g
.
Hen
ce
,
th
at
is
u
s
ed
as
th
e
SMS
-
T
M
b
ase
m
o
d
el,
wh
ich
u
s
es
s
am
p
le
d
ata
in
t
wo
p
ar
ts
.
Data
f
o
r
m
o
d
el
cr
ea
tio
n
an
d
a
s
ec
o
n
d
d
ata
s
et
to
ch
ec
k
its
p
r
ed
ictab
ilit
y
an
d
ac
cu
r
ac
y
.
I
t g
av
e
b
etter
r
esu
lts
,
an
d
as it is
a
m
ac
h
in
e
-
b
ased
m
o
d
el,
p
r
e
d
ictio
n
b
ec
o
m
es
m
o
r
e
ac
ce
s
s
ib
le
f
o
r
th
e
s
tak
eh
o
ld
er
s
w
h
o
wan
t
to
cr
ea
te
ab
n
o
r
m
al
r
etu
r
n
s
th
r
o
u
g
h
th
e
s
to
ck
m
ar
k
et.
T
h
e
SMS
-
T
M
m
o
d
el
h
elp
s
tr
ad
er
s
cr
ea
te
h
ig
h
er
r
etu
r
n
s
an
d
m
ak
es
th
e
m
ar
k
et
m
o
r
e
ef
f
icien
t
an
d
s
tab
le
to
p
r
e
v
en
t
th
e
ec
o
n
o
m
y
’
s
wea
lth
.
I
n
th
e
c
u
r
r
e
n
t
s
tu
d
y
,
o
n
ly
n
in
e
eq
u
ity
s
h
ar
es
f
r
o
m
th
e
I
n
d
ian
m
ar
k
e
t
ar
e
co
n
s
id
er
e
d
a
s
am
p
le,
b
u
t
o
n
e
ca
n
u
s
e
th
e
s
am
e
ap
p
r
o
ac
h
f
o
r
m
o
d
el
cr
ea
tio
n
f
o
r
m
o
r
e
eq
u
ity
s
h
ar
es
an
d
m
ar
k
ets
f
o
r
b
etter
r
esu
lts
.
T
h
is
is
n
o
t
o
n
ly
f
o
r
tr
ad
er
s
,
b
u
t
a
cu
r
r
en
t
p
iece
o
f
p
ap
e
r
also
h
el
p
s
th
e
g
o
v
er
n
m
e
n
t
cr
ea
te
a
m
o
r
e
s
tab
le
m
a
r
k
et
b
y
m
o
tiv
atin
g
v
ar
io
u
s
g
o
v
er
n
m
e
n
t sch
em
e
in
v
estme
n
ts
in
to
s
to
ck
m
ar
k
ets.
6.
CO
NCLU
SI
O
N
T
h
e
s
tu
d
y
’
s
r
esu
lts
alig
n
wi
th
p
r
ev
io
u
s
wo
r
k
’
s
ex
p
ec
ted
m
ar
k
et
p
r
ed
ictio
n
t
o
cr
ea
te
ab
n
o
r
m
al
r
etu
r
n
s
.
SMS
-
T
M
is
a
h
u
m
b
le
attem
p
t
to
cr
ea
te
a
b
etter
ac
c
u
r
ac
y
m
o
d
el
th
at
co
v
e
r
s
s
en
tim
en
t
an
d
tech
n
ical
p
ar
am
eter
s
.
As
d
is
cu
s
s
ed
in
t
h
e
r
esu
lts
,
a
h
y
b
r
id
m
o
d
el
g
iv
es
b
etter
r
esu
lts
th
an
in
d
iv
i
d
u
al
tech
n
iq
u
es
-
b
ased
buy
-
s
ell
p
r
e
d
ictio
n
th
r
o
u
g
h
K
NN,
DT
,
ANN,
SVM,
L
R
,
an
d
R
F.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
is
f
u
r
th
e
r
d
iv
id
ed
in
t
o
lay
er
0
an
d
la
y
er
1
,
wh
er
e
lay
e
r
0
u
s
es SVM
,
L
R
,
an
d
R
F
f
o
r
p
r
e
d
ictio
n
as
b
ase
lear
n
er
s
o
n
e,
two
an
d
th
r
ee
th
at
’
s
s
tak
in
g
o
f
th
e
m
o
d
el.
Fu
r
th
er
,
lay
e
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AUTHO
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DATA AV
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[
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3
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