Economic Determinants and Oil Shocks: Unravelling the Impact of
Kuala Lumpur Composite Index (KLCI) Performance
Dhia Damia Husni and Abd Hadi Mustaffa
a
Faculty of Business Management and Professional Studies, Management & Science University, University Drive,
Off Persiaran Olahraga, 40100 Shah Alam, Selangor, Malaysia
Keywords: Oil Shocks, Stock Market Performance, ARDL.
Abstract: The Kuala Lumpur Composite Index (KLCI) increase steadily from 1970 to 2023 despite major swings in
important economic indicators such as inflation, exchange rates, GDP, and oil shocks. This divergence
between economic issues and stock market performance emphasises the importance of delving deeper into
the underlying causes. This study examines the impact of economic forces and oil shocks on KLCI
performance using World Bank data from 1970 to 2023. The Auto Regression Distribution Lag (ARDL)
model was used to determine how these variables influence stock market performance in the short and long
run. Two findings are highlighted based on ARDL analysis. First, the short-run findings indicate that Oil Price,
GDP, and Exchange Rate positively impact KLCI. However, inflation delivers a significant and negative
impact on KLCI. Second, the long-run findings indicate that oil prices and GDP deliver significant and
positive impacts towards KLCI. However, inflation and exchange rates have significant and negative impacts
on KLCI. Those findings lead towards further discussion and policy recommendations in the later section,
aligning with SDG 7 (Affordable and clean energy) and 8 (Decent Work and economic growth).
1 INTRODUCTION
Studies on stock market performance have
consistently investigated the impact of various
economic and non-economic factors that determine
market results. These studies frequently concentrate
on components such as value, momentum, and
macroeconomic indicators, examining their impact
on stock returns. Surprisingly, stock markets have
exhibited resilience, occasionally exhibiting positive
returns during economic downturns, highlighting
their ability to survive economic adversities. In
Malaysia, the Kuala Lumpur Stock Exchange,
rebranded as Bursa Malaysia in 2004, is the principal
trading platform. The Kuala Lumpur Composite
Index (KLCI), a significant market performance
indicator, reflects larger economic trends and
provides useful insights into the market's overall
health (Chia et al., 2020).
The KLCI's trading volume trend from 1970 to
2023 shows an overall upward tendency, with notable
drops in individual years such as 1995, 2009, and
2021. These downturns were caused by both global
a
https://orcid.org/0000-0003-1919-2009
and domestic economic shocks, with the ASEAN
financial crisis of the late 1990s having a critical role
(Boonman, 2023). In addition to these crises, oil price
shocks had a substantial impact on KLCI
performance, demonstrating the intricate link
between global oil price changes and the Malaysian
market. This highlights the need to take into account
both macroeconomic forces and external influences,
such as oil price swings, when analyzing KLCI
patterns.
To fill this research gap, the study examines the
impact of both economic forces and oil shocks on the
short run and long run performance of the KLCI using
World Bank data from 1970 to 2023. This paper has
made some contributions. First is methodology
contribution, an econometric analysis, especially an
Auto Regressive Distributed Lag (ARDL) model,
will be used to assess the correlations between these
factors and KLCI performance. This model allows for
the investigation of both short- and long-term impacts
while accounting for any cointegration among the
variables. The study's goal is to investigate how these
parameters influence KLCI performance, both
Husni, D. D. and Mustaffa, A. H.
Economic Determinants and Oil Shocks: Unravelling the Impact of Kuala Lumpur Composite Index (KLCI) Performance.
DOI: 10.5220/0013429700003956
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 7th International Conference on Finance, Economics, Management and IT Business (FEMIB 2025), pages 243-250
ISBN: 978-989-758-748-1; ISSN: 2184-5891
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
243
separately and in combination, by taking into account
their interactions and correlations. Second, the study
contributes new perspectives by concentrating on
Malaysia's oil shocks and macroeconomic factors,
which have received little attention in rising
Southeast Asian economies (Nguyen & Pham, 2021;
Umar et al., 2023). The third contribution
incorporates SDG 8: Decent Work and Economic
Growth and SDG 7: Affordable and Clean Energy. By
examining the impact of oil shocks and
macroeconomic factors, this research offers policy
recommendations to promote sustainable economic
growth and energy efficiency while fostering resilient
financial systems that support inclusive economic
opportunities in Malaysia.
2 LITERATURE REVIEW
2.1 Underpinning Theory
The Efficient Market Hypothesis (EMH), a well-
known hypothesis in financial economics, asserts that
financial markets are informationally efficient, which
means that asset prices quickly reflect all available
information (Fama, 1970). Because price fluctuations
are mostly influenced by fresh information, which is
erratic and unpredictable, EMH contends that it is
practically impossible for investors to outperform the
market regularly. This idea is essential to comprehend
how the stock market behaves and how outside
influences, such as macroeconomic variables and
shocks connected to oil, affect market performance.
One of the primary principles of the EMH is the
idea that markets are efficient, which contradicts
conventional wisdom regarding investors' or fund
managers' capacity to "beat the market" by using
better strategy or analysis. Rather, the theory suggests
that stock values fluctuate in a "random walk,"
meaning it is impossible to forecast future changes
based on historical performance. According to EMH,
the market quickly prices in variables like GDP
growth, inflation, and oil price volatility, eliminating
any opportunity for arbitrage or excess gains
(Samuelson, 1965).
The EMH's critics argue that transaction costs,
market anomalies, and behavioural biases contribute
to inefficiencies that keep markets from operating
optimally. For example, short-term asset mispricing
may result from irrational investor behaviour, as
shown during market booms or collapses (Gupta et
al., 2025). This theory calls into question the fact that
genuine intrinsic values are always reflected in
markets. Furthermore, detractors point out that
regulatory restrictions, reduced liquidity, and
restricted information access may cause emerging
markets like Malaysia to deviate from efficiency
(Ahmad & Wu, 2022). The effect of outside shocks,
such as changes in the price of oil, on market
efficiency is another issue that the EMH framework
calls into question. Critics point out that although the
hypothesis posits that markets react to shocks fast, the
precision and speed of this adjustment might vary
based on the market's depth, maturity, and
information availability (El Ghoul et al., 2022).
2.2 Empirical Review
Empirical research on the effects of oil shocks and
macroeconomic variables on Malaysia's stock market
reveals disparities between short- and long-term
effects. Dutta et al. (2021) found that fluctuations in
oil prices had a considerable impact on stock returns.
The market reacts poorly during crises, such as the
COVID-19 epidemic, but recovers as oil prices rise.
Mokni (2020) also implies that demand-driven oil
shocks tend to stabilize stock returns in the long run,
whereas supply shocks often cause short-term
volatility.
The impact of oil prices on the stock market is
mixed. Aowa et al. (2023) found a negative long-term
impact caused by growing expenses for oil-dependent
enterprises. However, Siow et al. (2023) discovered
that Malaysia, as a significant oil exporter, benefits
from rising oil prices over time. Oil consumption is
also important, as more consumption is associated
with better stock performance. Alamgir and Amin
(2021) claimed that higher oil usage boosts national
GDP and investor confidence over time.
Inflation's effect on the stock market is more
complex. While Kalam (2020) reported a beneficial
influence on Malaysian stock indices, Sumaryoto et
al. (2021) discovered no meaningful short-term
impact. Inflation might reduce purchasing power in
the short term, but it also indicates economic stability
and growth. Meanwhile, GDP consistently correlates
positively with stock performance, with Li et al.
(2022) and Zulkifli et al. (2024) correlating economic
expansion to increased consumer spending and
market confidence. As Liu et al. (2023) point out,
GDP contractions often result in negative stock
returns. Finally, exchange rates have a variable
impact on stock markets across time. While Dang et
al. (2020) reported a long-term positive association
with Malaysia's KLCI, short-term volatility
frequently caused market instability.
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3 METHODOLOGY
3.1 Data Collection and Source
The relationship between Malaysia's stock market
performance, oil shocks, and key Macroeconomic
indicators has been intensively studied. This research
incorporates significant macroeconomic factors that
influence Malaysia's stock market performance. The
explained variable in this study is stock market
performance. Annual data spanning from 1970 to
2023 was collected from publicly available online
sources from the World Bank Development
Indicators (WDI), as presented in Table 1. The data
underwent a log transformation before conducting the
Auto Regression Distribution Lag (ARDL) bound test.
The data were log-transformed before the application
of the ARDL bound test.
3.2 Model Specification
The study employed the auto Regressive Distributed
Lag (ARDL) model initially developed by (Pesaran et
al., 2001) to examine the long-term and short-term
interactions among oil shocks, oil price, oil usage and
key Macroeconomic factors affecting the Malaysian
stock market performance. The methodology is
particularly valuable for analysing connections
among non-stationary time series data, including
variables that exhibit trends or random fluctuations.
The ARDL approach offers a significant advantage in
managing models with mixed-order integration,
accommodating variables that may exhibit varying
orders of integrations. Some variables may be
integrated into order one (I (1)), indicating the
presence of a unit. Root and non-stationery are
present, whereas others may exhibit zero (I (0))
integration, indicating stationery. Consequently, it is
possible to examine relationships among variables
using different statistical properties.
The ARDL approach allows researchers to
estimate the long-term equilibrium relationships
between variables while capturing short-term
dynamics or adjustments towards equilibrium. This
methodology facilitates the assessment of the impact
or changes in one variable on another over time, even
in the presence of non-stationary variables. This
model was constructed to establish the relationship
between the dependent and independent variables.
Where LnKLCI is the Stock market performance
in period t, LnOILRENTS is the
oil rents (% of GDP)
in period t, OILPRI is the oil pump price in period t,
OILUSG is the domestic oil usage in period t, IF is
the inflation rate in period t, GDP is gross domestic
product in period t, and lastly MYR is Malaysian
ringgit exchange rate in period t. In addition, β_0 is a
constant, β_1 to β_6 are the respective regression
coefficients, ε_ denotes stochastic white noise, and t
denotes the period (1970 to 2023).
3.3 Unit Root Test (Test for
Stationarity)
Studies indicate that unit roots may result in
erroneous conclusions in time series analysis
(Granger & Newbold, 1974; Philips, 1987). The
Augmented Dicky-Fuller (ADF) and Philips- Perron
(PP) tests have been established to evaluate the
stationarity of standard unit roots (Dicky & Fuller,
1979; Perron, 1990). Unit root tests are essential for
assessing the integration order variables or series that
are predominantly non-stationary. The specified
model (Equation 3) assumes that the variables exhibit
unit roots at the initial level. These variables can be
converted into stationary via first differencing,
represented as (I (1)). The ADF and PP tests can be
utilised to assess the existence of a unit root in this
scenario. The standard formulation of the ADF test is
expressed in Equation 1:
(1)
Table 1: Variable description and data source.
Variables Descriptions Measurement units Source
KLCI Mala
y
sian stoc
k
market
p
erformance Stoc
k
traded, total value
(
%ofGDP
)
WDI
OILRENTS Oil shocks Oil rents (% of GDP) WDI
OILPRI Oil
p
rice Pum
p
p
rice fo
r
g
asoline
(
US$
p
e
r
litre
)
WDI
OILUSG Oil usage Energy use (kg of oil equivalent
p
e
r
capita) WDI
IF Inflation Inflation, consume
r
p
rices
(
annual %
)
WDI
GDP Gross Domestic Product GDP growth (annual %) WDI
MYR Exchan
g
e Rate Official exchan
g
erate
(
LCU
p
e
r
US$,
p
erio
d
avera
g
e
)
WDI
Economic Determinants and Oil Shocks: Unravelling the Impact of Kuala Lumpur Composite Index (KLCI) Performance
245
Exogeneity test was not judged necessary in this
study because it used the Auto Regressive Distributed
Lag (ARDL) model. The ARDL model is resistant to
endogeneity problems, particularly when variables of
order I(0) or I(1), as in the case of the selected
independent variables. Furthermore, the factors
chosen—such as oil price shocks, GDP, inflation, and
currency rates—are predominantly impacted by
broader macroeconomic dynamics and are unlikely to
be considerably affected by KLCI performance,
particularly in the short run. As a result, exogeneity
problems are negligible in this setting, and rigorous
testing was not necessary. However, if any
endogeneity is discovered throughout the analysis,
suitable corrective steps will be implemented.
3.4 ARDL Model
The variables identified by the ARDL bounds test
exhibit a common stochastic trend and will increase
proportionally over time. Engle and Granger (1987)
propose that an error correction model (ECM) is
warranted when a long-term relationship exists
between the variables. The vector autoregression
model (VAR) is generally favoured when the
variables demonstrate a short-term relationship. In the
context of cointegration, an ECM representation can
be articulated through equations 3.
Where
represents the Malaysia stock
market performance,
denotes the residual
series from the long-run regression error correction
term, is the parameter for the speed of adjustment,
is the difference operator,
is the constant to
represent the regression coefficients, n represents the
optimal lag orders, and I represent the number of
variables in the model, which could be from 1 to k.
The dependent variable is a function of its lagged
values
, which becomes an exogenous
variable among the legged values of the regressors in
the model.
The ARDL form for the long-run relationship
model in the context of cointegration is specified in
equation (2):
The short-run model, estimated with ECM, is
formulated as in equation (3):
4 RESULTS AND DISCUSSIONS
4.1 Descriptive Results
Table 2 contains descriptive statistics on oil shocks,
oil prices, oil usage, and major economic factors that
influenced Malaysia's stock market performance
from 1970 to 2023. The KLCI has a mean of 5.4018
and a standard deviation of 6.1850, indicating high
market volatility caused by macroeconomic
conditions, financial crises, and fluctuations in
investor opinion.
Oil-related variables fluctuate at different rates.
Oil shocks (OILRENTS) have a mean of 4.758 and a
standard deviation of 3.087, suggesting Malaysia's
vulnerability to geopolitical risks and global supply-
demand imbalances. In contrast, oil prices (OILPRI)
showed less variability (mean: 0.442, SD: 0.0851),
indicating that global pricing systems have partially
stabilized excessive volatility. However, given
Malaysia's reliance on oil, even small price
fluctuations can have an impact on the stock market.
Aside from oil, other economic indices show
noticeable movements. Oil usage (OILUSG) has a
mean of 2514.932 and a standard deviation of
777.841, indicating that it responds to industrial
activity and economic cycles. Inflation (IF) is very
variable (mean: 3.358, SD: 2.793), influenced by both
domestic and global economic influences.
Meanwhile, GDP growth has been reasonably
consistent (mean: 6.617, SD: 2.657), demonstrating
Malaysia's resilience in the face of global and local
economic shifts. The currency rate (MYR), with a
mean of 3.128 and a standard deviation of 0.693,
shows moderate volatility, which is critical for
investor confidence and trade competitiveness.
Aside from oil, other economic indices show
noticeable movements. Oil usage (OILUSG) has a
mean of 2514.932 and a standard deviation of
777.841, indicating that it responds to industrial
activity and economic cycles. Inflation (IF) is very
variable (mean: 3.358, SD: 2.793), influenced by both
domestic and global economic influences.
Meanwhile,
GDP growth has been reasonably
(2)
(3)
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Table 2: Descriptive Statistics.
Variables Observ Mean St. Dev Minimum Maximum
Stoc
k
market
p
erformance
(
KLCI
)
54 5.402 6.185 1.172 2.491
Oil shocks (OILRENTS) 54 4.758 3.087 0.017 13.334
Oil
p
rice
(
OILPRI
)
54 0.442 0.085 0.280 0.680
Oil usage (OILUSG) 54 2514.932 777.841 1029.126 3500.000
Inflation
(
IF
)
54 3.358 2.793 0.290 17.3289
Gross domestic
p
roduct (GDP) 54 6.6172 2.6574 0.5177 11.7011
Exchan
g
e rate
(
MYR
)
54 3.1284 0.6928 2.1769 4.4011
Note: The results are taken before using an algorithm. KLCI, OILRENTS, OILPRI, OILUSG, IF, GDP and MYR represent Stock Market
Performance, Oil shocks, Oil Prices, Oil Usage, Inflation, Gross domestic product and Exchange Rate.
Table 3: Unit root test via Augmented Dicky-fuller (ADF) and Philips-Perron (PP).
ADF Philli
p
s-Perron
T-STAT At level First difference
Variables At level First difference Z(
p
) Z(t) Z(
p
) Z(t)
KLCI -2.12 -11.48*** 0.08* -3.29 0.0000*** -11.48
OILRENTS -6.53 -4.66*** 0.00*** -7.69 0.0001*** -5.66
OILPRI -2.59 -4.50*** 0.65 -1.88 0.0355** -3.65
OILUSG -1.48 -6.28*** 0.93 -1.06 0.0000*** -10.65
IF -4.51 -9.23*** 0.003*** -4.51 0.0000*** -10.23
GDP -6.34 -11.02*** 0.00*** -6.33 0.0001*** -37.44
MYR -3.62 -5.59*** 0.15 -2.95 0.0002*** -5.48
consistent (mean: 6.617, SD: 2.657), demonstrating
Malaysia's resilience in the face of global and local
economic shifts. The currency rate (MYR), with a
mean of 3.128 and a standard deviation of 0.693,
shows moderate volatility, which is critical for
investor confidence and trade competitiveness.
4.2 Unit Root Test Results (Test for
Stationery)
Before implementing the ARDL test, confirm that
each variable's unit root is stationary at I(0), I(1), or
both for the bound F-statistical test. The integration
order of the variables was determined using the
augmented Dicky-Fuller (ADF) and Philips-Perron
(PP) unit root tests (Dicky & Fuller, 1979; Perron,
1990), and the results were compared to Mackinnon's
(1996) critical values. The null hypothesis implies the
existence of a unit root, and a test statistic less than
the critical value demonstrates stationarity. As shown
in Table 3, the ADF and PP tests show that all
variables (KLCI, OILRENTS, OILPRI, OILUSG, IF,
GDP, and MYR) are stationary at I(1) with a 0.05
significant level. This implies that there is no unit
root, showing that the variables follow a stable or
predictable pattern, and that they have the same order
of stationarity, implying a possible long-run link.
4.3 Short Run Estimation
Engle & Granger (1987) assert that an Error
Correction Model (ECM) should be developed when
a long-run relationship exists between variables. As a
result, ECM was created to estimate the error term.
The estimated coefficient of ARDL-ECM indicates
the existence of cointegration among variables and
exhibits a positive sign. It means the adjustment rate
to long-run equilibrium after experiencing short-run
disturbances. The estimated ECM coefficient in this
study is - 0.02, which is statistically significant at the
5% level. This finding indicates that any divergence
from short-run equilibrium among variables and
Stock Market performance can be corrected and
reinstated annually at 0.02% in the long run, as
demonstrated in Table 4. Following the verification
of long and short-run associations between variables
through the ARDL test, the study identifies the
parameters for these variables in both time frames.
The short-run ARDL model finds numerous
significant drivers of Malaysia's stock market
performance (DLnKLCI). Oil price (DLnOILPRI),
lagged by one and two periods, has a substantial
negative impact on KLCI, with a coefficient of
3.6202 (p = 0.0380), showing that higher oil prices in
the past have harmed stock market performance.
Lagged inflation (DLnIF) spanning four periods
likewise demonstrates a negative association, with a
coefficient of -0.1940 (p = 0.0323), implying that
higher historical inflation damages the market by
Economic Determinants and Oil Shocks: Unravelling the Impact of Kuala Lumpur Composite Index (KLCI) Performance
247
Table 4: Estimated Short-run coefficient from ARDL (54 observations from 1970 to 2023).
DLnKLCI Coefficient Std. Error T-statistics Prob Results Impact
DLnOILRENTS 0.0211 0.1286 0.1641 0.8707 Not significant -
DLnOILPRI (-1) (-2) 3.6202 1.6706 -2.1669 0.0380 Significant Positive
DLnOILUSG -0.2729 0.5380 -0.5072 0.6155 Not significant -
DLnIF (-1) (-2)(-3)(-4) - 0.1940 0.08657 -2.2415 0.0323 Significant Negative
DLnGDP (-1) (-2) 0.2370 0.1168 2.0288 0.0511 Significant Positive
DLnMYR (-1) 2.1621 0.8074 2.6778 0.0117 Significant Positive
ECM -0.0174 0.0029 -5.9214 0.0000 Significant Positive
*P>|z| values are based on a 5% significant level. The dependent variable is Malaysia's stock market performance (KLCI). ARDL
(3,0,4,2,1,2,0) was selected using Akaike information criteria (Source: Author's Estimation).
Table 5: Estimated Long-run coefficient from ARDL (54 observations from 1970 to 2023).
LnKLCI Coefficient Std. Error T-statistics Prob Results Impact
LnOILRENTS 0.0211 0.1285 0.1641 0.8707 Not significant -
LnOILPRI 2.3587 1.0323 2.2848 0.0293 Si
g
nificant Positive
LnOILUSG -0.2729 0.5380 -0.5073 0.6155 Not significant -
LnIF -0.2966 0.1195 -2.4823 0.0187 Si
g
nificant Ne
g
ative
LnGDP 0.2580 0.1094 2.3589 0.0248 Significant Positive
LnMYR -2.5028 0.8905 -2.8104 0.0085 Si
g
nificant Ne
g
ative
*P>|z| values are based on a 5% significant level. The dependent variable is Malaysia's stock market performance (KLCI). ARDL
(3,0,4,2,1,2,0) selected by Akaike information criteria (Source: Author's Estimation)
reducing consumer spending and purchasing power.
Gross Domestic Product (DLnGDP), which is lagged
by one or two periods, has a positive correlation with
stock market performance, with a coefficient of
0.2370 (p = 0.0511), demonstrating that economic
expansion stimulates the market. The exchange rate
(DLnMYR) also has a positive effect, with a
coefficient of 2.1621 (p = 0.0117), implying that a
stronger Malaysian Ringgit boosts stock market
performance. The ECM coefficient of -0.0174 (p =
0.0000) indicates that short-term deviations from
long-run equilibrium are rectified at a rate of 1.74%
each cycle.
In contrast, oil shocks (OILRENTS) and oil
consumption (DLnOILSUG) had no significant
impact on KLCI performance, with coefficients of
0.0211 (p = 0.8707) and -0.2729 (p = 0.6155),
respectively, indicating that changes in oil prices or
usage do not instantly affect Malaysia's stock market.
4.4 Long Run Estimation
The long-run ARDL estimation results based on
Table 5 indicate several significant relationships
between all variables and Malaysia's stock market
performance from 1970 to 2023. Oil price
(LnOILPRI) exhibits a significant positive effect on
LnKLCI, indicated by the coefficient of 2.3587 (p =
0.0293). A 1% increase in LnOILPRI results in a
2.36% increase in LnKLCI, highlighting the oil
price's significance in influencing the Malaysian
stock market performance. Inflation (LnIF)
significantly and negatively impacts LnKLCI,
indicated by a coefficient of -0.2966 (p = 0.0187).
Gross Domestic Product (LnGDP) exhibit a
significant and positive correlation with LnKLCI,
evidenced by a coefficient of 0.2580 (p = 0.0248),
suggesting an increase in gross domestic product
results in a 0.26% increase in Malaysia stock market
performance. The exchange rate (LnMYR) exhibits a
significant and negative impact on LnKLCI, indicated
by the coefficient of -2.5028 (p = 0.0085), suggesting
that a 1% depreciation in the Malaysian Ringgit
(LnMYR) leads to a 2.50% decline in the Malaysia
stock market performance. This finding underscores
the sensitivity of the stock market to currency
fluctuations, where a weaker exchange rate
negatively impacts investor confidence and stock
market valuations.
Conversely, various variables exhibit no
significant long-term effect on LnKLCI. The oil
shocks (LnOILRENTS) with a coefficient of 0.0211
(p = 0.8707) do not significantly influence the
Malaysia stock market performance, indicating that
fluctuations in the oil shocks do not impact LnKLCI
over a long time. Likewise, Oil Usage (LnOILUSG),
exhibiting a coefficient of -0.2729 (p = 0.6155),
indicates no significant relationship, implying that oil
usage does not directly affect Malaysia's stock market
performance.
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4.5 Residual Diagnostics Check
Table 6 represents the residual diagnostics check
pertinent to the residual analysis of the ARDL model.
The model produced an R-square of 0.973297,
signifying a strong fit, as it accounts for
approximately 97.3% of the variability of the
Malaysian stock market performance (KLCI). The
adjusted R-Square values of 0.9578 indicated a robust
fit, considering the number of predictors in the model,
and demonstrated that approximately 95.8% of the
variations are explained post-adjustment. The
Durbin-Watson of 2.1869 indicates an absence of
significant autocorrelations in the residuals, as it is
approximately the ideal value of 2, signifying that the
errors are not serially correlated.
Table 6: Diagnostic Tests.
R- Square 0.9733
Ad
j
uste
d
R-S
q
uare 0.9578
Durbin-Watson statistics 2.1869
5 CONCLUSION
The study aims to provide insights into how oil
shocks, oil price fluctuations, oil usage, and key
macroeconomic variables influence Malaysia's stock
market performance in the short and long run and
their broader implications for economic stability and
policy formulation. Data for this analysis were
gathered from reliable sources, including the World
Bank and the Central Bank of Malaysia, from 1970 to
2023. The study employs the Auto Regression
Distributed Lag (ARDL) approach to investigate the
dynamic relationships and causality between these
variables and stock market performance, offering
valuable findings for policymakers and investors.
Two findings are highlighted based on ARDL
analysis. First, the short-run findings indicate that Oil
Price, GDP, and Exchange Rate positively impact
KLCI. However, inflation delivers a significant and
negative impact on KLCI. Second, the long-run
findings indicate that oil prices and GDP deliver
significant and positive impacts towards KLCI.
However, inflation and exchange rates have
significant and negative impacts on KLCI. Those
findings lead towards further discussion and policy
recommendations in the later section.
5.1 Policy Recommendation
The findings of this study present crucial policy
recommendations for improving Malaysia's stock
market stability in line with SDG 8, which focuses on
promoting economic growth and decent work. In the
medium term, stabilizing oil prices and lowering
reliance on fossil fuels are crucial for mitigating the
negative consequences of price volatility. Malaysia
should focus on energy diversification by investing in
renewable energy sources including solar, wind, and
hydropower. This will help to achieve SDG 7
(Affordable and Clean Energy) while also protecting
the economy from cyclical changes in oil prices. The
government may encourage energy sector innovation,
provide incentives for green technologies, and drive
private sector involvement in sustainable energy, all
of which would improve the economy's long-term
resilience.
To lessen dependency on global oil price
volatility, Malaysia must prioritize economic
diversification beyond the oil and gas industry in the
long run. Promoting GDP development through
investments in high-value areas like technology,
digital innovation, and green industries will help to
stabilize the stock market and promote SDG 8's aims
of inclusive and sustainable industrialisation.
Furthermore, fostering a business-friendly climate for
new industries, as well as investing in education and
skill development in the green economy, can help
Malaysia create jobs and maintain its global
competitiveness. Regional and global collaborations
are particularly important because they can supply the
resources, knowledge, and money required to
transition to a sustainable economy, which benefits
both SDGs 7 and 8.
5.2 Limitations and Future Research
Directions
Based on the result, this study has weaknesses
whereby two variables that do not have short-run and
long-run effects are oil shocks and oil usage. Hence,
future research directions are to revise the formula to
calculate oil shocks and usage or use a new proxy for
the variable. In addition, scholars can also test other
variables available in world development indicators
that may impact the Malaysian stock market
performance, such as foreign direct investment,
export and import, etc., because they are believed to
be another factor affecting the Malaysian stock
market. One of the limitations of ARDL is that it only
analyses one-way directions. Hence, it is
recommended that future research test analysis using
Economic Determinants and Oil Shocks: Unravelling the Impact of Kuala Lumpur Composite Index (KLCI) Performance
249
NARDL, which can provide two-way directions, or
QARDL, which is based on quantile regression
ARDL. Those analyses might deliver more robust and
reliable results in the future.
ACKNOWLEDGEMENTS
We thank the Research Management Centre (RMC),
Management and Science University (MSU) for
supporting this research work via MSU Conference &
Seminar Grant (MCSG) funding with reference
number MCSG-025-042024.
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