A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting
ACM Computing Surveys,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 18, 2025
Artificial
Intelligence
(AI)
models
have
reached
a
very
significant
level
of
accuracy.
While
their
superior
performance
offers
considerable
benefits,
inherent
complexity
often
decreases
human
trust,
which
slows
application
in
high-risk
decision-making
domains,
such
as
finance.
The
field
eXplainable
AI
(XAI)
seeks
to
bridge
this
gap,
aiming
make
more
understandable.
This
survey,
focusing
on
published
work
from
2018
2024,
categorizes
XAI
approaches
that
predict
financial
time
series.
In
paper,
explainability
and
interpretability
are
distinguished,
emphasizing
the
need
treat
these
concepts
separately
they
not
applied
same
way
practice.
Through
clear
definitions,
rigorous
taxonomy
approaches,
complementary
characterization,
examples
XAI’s
finance
industry,
paper
provides
comprehensive
view
current
role
It
can
also
serve
guide
for
selecting
most
appropriate
approach
future
applications.
Язык: Английский
ALL diagnosis: can efficiency and transparency coexist? An explainble deep learning approach
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 14, 2025
Acute
Lymphoblastic
Leukemia
(ALL)
is
a
life-threatening
malignancy
characterized
by
its
aggressive
progression
and
detrimental
effects
on
the
hematopoietic
system.
Early
accurate
diagnosis
paramount
to
optimizing
therapeutic
interventions
improving
clinical
outcomes.
This
study
introduces
novel
diagnostic
framework
that
synergizes
EfficientNet-B7
architecture
with
Explainable
Artificial
Intelligence
(XAI)
methodologies
address
challenges
in
performance,
computational
efficiency,
explainability.
The
proposed
model
achieves
improved
accuracies
exceeding
96%
Taleqani
Hospital
dataset
95.50%
C-NMC-19
Multi-Cancer
datasets.
Rigorous
evaluation
across
multiple
metrics-including
Area
Under
Curve
(AUC),
mean
Average
Precision
(mAP),
Accuracy,
Precision,
Recall,
F1-score-demonstrates
model's
robustness
establishes
superiority
over
state-of-the-art
architectures
namely
VGG-19,
InceptionResNetV2,
ResNet50,
DenseNet50
AlexNet
.
Furthermore,
significantly
reduces
overhead,
achieving
up
40%
faster
inference
times,
thereby
enhancing
applicability.
To
opacity
inherent
Deep
learning
(DL)
models,
integrates
advanced
XAI
techniques,
including
Gradient-weighted
Class
Activation
Mapping
(Grad-CAM),
(CAM),
Local
Interpretable
Model-Agnostic
Explanations
(LIME),
Integrated
Gradients
(IG),
providing
transparent
explainable
insights
into
predictions.
fusion
of
high
precision,
explainability
positions
as
transformative
tool
for
ALL
diagnosis,
bridging
gap
between
cutting-edge
AI
technologies
practical
deployment.
Язык: Английский
Decision‐Making in M&A Under Market Mispricing: The Role of Deep Learning Models
Managerial and Decision Economics,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 4, 2025
ABSTRACT
In
the
ever‐evolving
landscape
of
financial
markets,
mergers
and
acquisitions
(M&A)
play
a
pivotal
role
in
shaping
corporate
ecosystem.
However,
presence
market
mispricing,
driven
by
various
factors
such
as
information
asymmetry,
behavioral
biases,
external
shocks,
has
been
persistent
challenge
for
investors
corporations
alike.
Understanding
intricate
relationship
between
stock
mispricing
M&A
is
crucial
making
informed
investment
decisions
fostering
resilient
environment.
This
research
explores
how
impacts
within
fragmented
setting,
utilizing
deep
learning
methods
to
uncover
complex
patterns
relationships.
By
analyzing
inefficiencies,
study
aims
provide
deeper
understanding
influences
strategies
outcomes.
Employing
quantitative
descriptive
design,
gathered
valid
data
through
distributed
questionnaires,
yielding
responses
from
130
traders,
115
participants,
99
regulators
policymakers.
The
analysis
was
conducted
using
Statistical
Package
Social
Sciences
(SPSS).
Firstly,
it
establishes
effectiveness
algorithms
detecting
quantifying
providing
reliable
measure
its
extent.
then
differential
performance
outcomes
companies
engaging
during
periods
prevalent
compared
those
efficient
pricing.
study's
novel
contribution
lies
introduction
sentiment
models
incorporate
participants'
sentiments,
enhancing
accuracy
detection
impact
on
activity.
Finally,
this
contributes
valuable
insights
into
integration
techniques
leveraging
strategic
decision‐making
context
M&A.
Язык: Английский
Investigating the impact of sentiments on stock market using digital proxies: Current trends, challenges, and future directions
Expert Systems with Applications,
Год журнала:
2025,
Номер
285, С. 127864 - 127864
Опубликована: Май 5, 2025
Язык: Английский
Modelling and forecasting of Nigeria stock market volatility
Future Business Journal,
Год журнала:
2025,
Номер
11(1)
Опубликована: Май 29, 2025
Abstract
This
study
models
and
forecasts
the
volatility
of
Nigerian
Stock
Exchange
(NSE)
using
advanced
econometric
techniques,
focusing
on
examining
asymmetric
leverage
effect.
Daily
data
from
NSE
All
Share
Index
spanning
30
th
January,
2012,
to
16
October,
2024
(3,176
days)
are
analysed
generalized
autoregressive
conditional
heteroskedasticity
family
models,
including
EGARCH
GJR-GARCH,
along
with
non-Gaussian
distributions
like
Student’s
t-distribution.
The
findings
reveal
a
significant
effect,
where
negative
shocks
impact
stock
prices
more
than
positive
ones,
supporting
theory.
also
identifies
clustering,
high-volatility
periods
followed
by
continued
volatility,
further
highlighting
persistence
market
turbulence.
Among
tested,
GJR-GARCH
t-distribution
performs
best
in
forecasting
providing
superior
fit
accuracy.
These
insights
offer
practical
implications
for
investors
policymakers
managing
risks
emerging
markets,
particularly
during
high
volatility.
Язык: Английский
The effectiveness of using artificial intelligence in investment strategies on the stock markets
Finance and Credit,
Год журнала:
2025,
Номер
31(5), С. 89 - 107
Опубликована: Май 29, 2025
Subject.
This
article
discusses
the
effectiveness
of
using
artificial
intelligence
in
investment
strategies
Russian
and
American
stock
markets.
Objectives.
The
aims
to
assess
building
portfolios
managing
assets,
relying
on
key
metrics
performance.
Methods.
For
study,
we
used
methods
analysis
comparative
assessment
financial
asset
portfolio
management,
as
well
infographics.
Results.
finds
that
regarding
market,
only
one
out
five
examined
has
a
statistically
significant
positive
alpha
coefficient.
At
same
time,
index
hedge
funds
market
also
does
not
show
advantage
over
broader
market.
Conclusions
Relevance.
concludes
implementation
currently
significantly
increase
return
outperform
benchmark,
however,
this
may
change
with
alteration
time
horizon
for
such
strategies.
results
study
are
advancing
academic
research
effects
use
They
can
be
applied
later
development
optimization
intelligence,
assessing
their
investors.
Язык: Английский