Emerging Markets Finance and Trade,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 19
Published: Nov. 12, 2024
The
study
on
the
relationship
between
shadow
banking
(SB)
and
stock
market
volatility
is
scarce.
Based
sample
data
from
January
2006
to
May
2024,
this
paper
dives
deep
clarify
whether
how
China's
SB
brings
uncertainties
impact
of
stock-money
correlation.
novelty
that
we
connect
low-frequency
with
high-frequency
financial
information
under
framework
mixed-frequency
analysis.
Our
findings
indicate
expansion
could
directly
enhance
volatility.
There
exists
a
persistent
correlation
money
markets
in
long
run.
Furthermore,
rapid
development
also
strengthens
Since
reduce
controllability
monetary
policy
increase
uncertainties,
believe
may
serve
as
an
important
channel
for
spread
risks
by
pushing
two
move
more
closely.
We
shed
new
light
literature
regarding
market.
This
makes
first
attempt
identify
role
driving
dynamics
Journal of Futures Markets,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 7, 2025
ABSTRACT
The
launch
of
the
Shanghai
International
Energy
Exchange
crude
oil
futures
(INECOFs)
is
a
milestone
in
China's
path
to
dominant
position
global
energy
market.
As
INECOFs
attract
more
and
investors,
understanding
long‐term
correlations
between
regional
benchmarks,
as
well
driving
forces
these
correlations,
paramount
interest
investors
wishing
conduct
risk
management
portfolio
diversification.
This
article
makes
first
attempt
explore
determinants
such
using
mixed‐frequency
approach.
Our
results
show
that
are
highly
correlated
with
benchmarks
less
benchmarks.
imports,
RMB
internationalization,
index,
economic
trade
policy
uncertainty,
geopolitical
risks
significantly
impact
dynamics
question.
gross
industrial
product
price
levels
cannot
drive
movements
all
studied
correlations.
Financial Innovation,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: May 20, 2024
Abstract
Using
data
on
Chinese
non-financial
listed
firms
covering
2009
to
2022,
we
explore
the
effect
of
supply
chain
transparency
stock
price
crash
risk.
Two
proxies
for
are
constructed
using
number
partners’
names
and
proportion
their
transactions
disclosed
in
annual
reports.
The
results
reveal
that
enhancing
can
decrease
risk,
specifically
by
mitigating
tax
avoidance
earnings
management.
Moreover,
analysis
suggests
this
risk-reduction
is
more
prominent
companies
where
managers
incentivized
hide
negative
information
investors
possess
superior
abilities
acquire
information.
Interestingly,
supplier
influential
risk
than
customer
transparency.
These
findings
emphasize
significance
managing
financial
Alexandria Engineering Journal,
Journal Year:
2024,
Volume and Issue:
95, P. 94 - 100
Published: April 1, 2024
Financial
engineering
is
crucial
for
effectively
combining
finance
with
quantitative
approaches.
This
study
aims
to
forecast
the
performance
of
Nasdaq
stock
market
by
considering
numerous
factors
like
wind,
hydro,
thermal,
gas,
and
nuclear
variables.
To
accomplish
this,
we
utilize
sophisticated
predictive
models,
namely
adaptive
lasso
(ALasso),
elastic
net
(Enet),
artificial
neural
network
(ANN),
convolutional
(CNN),
long
short-term
memory
(LSTM).
By
using
these
advanced
methods,
our
goal
offer
perceptive
precise
predictions,
which
will
enhance
comprehension
complex
dynamics
within
financial
markets.
The
evidence
suggests
that
LSTM
model
has
demonstrated
superior
accuracy
in
predicting
changes
when
compared
ALasso,
Enet,
ANN,
CNN.
While
CNN
exhibit
comparable
RMSE
MAE
values,
their
slightly
less
competitive
than
model.
marginal
differences
(ALasso:
0.319,
Enet:
0.317,
ANN:
0.3,
CNN:
0.32)
0.277,
0.276,
0.252,
0.278)
emphasize
effectiveness
various
but
they
somewhat
drop
below
terms
precision.
findings
showed
significance
well-known
ML
techniques,
particularly
LSTM,
enhanced
predictions.
