Investment Analysts Journal,
Год журнала:
2024,
Номер
unknown, С. 1 - 18
Опубликована: Июнь 27, 2024
After
the
collapse
of
equity
market
in
early
2000s,
question
drivers
financial
assets
returns
preoccupied
interest
investors
and
policymakers
markets.
Thus,
this
study
explores
how
newly
developed
Cable
News-based
Economic
Policy
Uncertainty
(TVEPU)
predicts
major
using
daily
data
from
1
January
2014
to
30
September
2023.
To
achieve
objective,
we
introduced
Rolling
Windows
Wavelet
Quantile
Granger
Causality
(RWWQGC)
test.
The
empirical
results
show
that
TVEPU
tends
have
predictive
power
for
SP500
across
time,
frequency,
quantile.
also
has
a
strong
causal
impact
on
However,
US
10-year
bond,
dollar
index,
Bitcoin
is
weak
Based
these
results,
policy
recommendations
are
offered.
International Journal of Financial Studies,
Год журнала:
2025,
Номер
13(1), С. 28 - 28
Опубликована: Фев. 25, 2025
Accurately
predicting
stock
market
movements
remains
a
critical
challenge
in
finance,
driven
by
the
increasing
role
of
algorithmic
trading
and
centrality
financial
markets
economic
sustainability.
This
study
examines
incorporation
artificial
intelligence
(AI)
machine
learning
(ML)
technologies
to
address
gaps
identifying
predictive
factors,
integrating
diverse
data
sources,
optimizing
methodologies.
Employing
systematic
review,
recent
advancements
ML
techniques
like
deep
learning,
ensemble
methods,
neural
networks
are
analyzed,
alongside
emerging
sources
such
as
traders’
sentiment
real-time
indicators.
Results
highlight
potential
unified
datasets
adaptive
models
enhance
prediction
accuracy
while
overcoming
volatility
heterogeneity.
The
research
underscores
necessity
innovative
advanced
develop
robust
adaptable
forecasting
frameworks.
These
findings
offer
valuable
insights
for
academics
professionals,
paving
way
more
reliable
that
can
decision-making
dynamic
environments.
contributes
advancing
sustainability
proposing
methodologies
align
with
complexities
rapid
evolution
modern
markets.
