Analyzing The Impact of Macroeconomic Factors on Stock Market Performance in Asean-5 Countries

The study investigates how macroeconomic variables influence stock market performance in ASEAN-5 (Malaysia, Indonesia, Singapore, Thailand, Philippines), particularly during the COVID-19 period. Prior literature emphasizes links between stock markets and macroeconomic factors such as interest rates, exchange rates, inflation (CPI), and industrial production. However, much of the evidence focuses on industrialized economies, leaving a gap for emerging ASEAN markets. The authors hypothesize that macroeconomic variables significantly affect ASEAN-5 stock market fluctuations, with heightened relevance during COVID-19, and aim to quantify both correlation and short-run causal dynamics using panel econometrics. The purpose is to provide insights for investors and policymakers on how macroeconomic conditions and pandemic shocks transmit to stock indices, improving investment decisions, risk management, and policy design.

The paper reviews extensive evidence on macroeconomic determinants of stock markets. Studies across various contexts find that interest rates, inflation (CPI), exchange rates, oil prices, and industrial production are key drivers of stock returns (e.g., Farah et al., 2018; Nordin et al., 2014; Flannery & Protopapadakis, 2002). During COVID-19, several works report significant adverse effects on stock returns and heightened volatility (Baker et al., 2020; Bora & Basistha, 2021; Kamaludin et al., 2021; Liu et al., 2022), though findings are mixed across regions and periods (Rahman et al., 2021; Al-Awadhi et al., 2020; Yousfi et al., 2021). Prior ASEAN-specific and country studies show varied impacts of macro variables on stock indices, with some evidence of negative links for inflation and exchange rates and positive links for industrial production. The review also covers evidence from the SARS outbreak, indicating heterogeneous short-term market reactions across sectors and countries. Overall, the literature suggests macroeconomic conditions materially influence stock markets, but sign and magnitude can differ by period, region, and sector, justifying fresh analysis for ASEAN-5 during 2012–2022 and the COVID-19 shock.

• Research design: Empirical panel analysis using fixed effects (FEM), random effects (REM), and dynamic panel System GMM to assess short-run relationships between macroeconomic variables and stock market index across ASEAN-5.
• Sample: ASEAN-5 countries (Malaysia, Indonesia, Singapore, Thailand, Philippines), monthly observations from January 2012 to December 2022.
• Variables:
  • Dependent: Stock Market Index (SMI; 2010=100; Financialng.com).
  • Regressors: Interest rate INT (%) (10-year government bond yield; Euromonitor International), Consumer Price Index CPI (2010=100; Euromonitor International), Exchange rate EXR (CCY per USD; Investing.com), Industrial Production Index IPI (2010=100; Euromonitor International). All at country level.
• Preliminary analysis: Descriptive statistics reported. Visual inspection indicated non-stationarity at levels. Panel unit root tests (LLC, IPS, ADF-Fisher, PP-Fisher) applied at levels and first differences; variables are stationary at first differences (I(1)), with one potentially I(0) per table notes.
• Model specification:
  • Baseline panel model: Y_it = α + Σ β_j Z_ik + μ_it, augmented with lagged dependent variable: Y_it = α + δ Y_it−1 + Σ β_j Z_ik + μ_it, where Z includes INT, CPI, EXR, IPI.
  • Estimators: FEM and REM estimated; Hausman test used to select between them.
  • Dynamic panel: Two-step System GMM (Arellano-Bond 1991; Blundell-Bond 1998; Arellano-Bover 1995) with lagged dependent variable as regressor; instruments are lagged levels and differences; diagnostics include Sargan test of overidentifying restrictions and Arellano–Bond AR(1), AR(2) tests for serial correlation.
• Software: E-views 12.

• Descriptive statistics (levels): Mean SMI 156.38; INT 3.615%; CPI 121.21; EXR mean 2633.89 (wide dispersion); IPI 123.16. Visual plots indicate non-stationarity at levels; first differences appear stationary.
• Correlation (levels): Highest correlations include LEXR–INT (0.7883) and LEXR–LCPI (0.6520). First differences correlations are generally small; D(LSMI) and D(LEXR) show negative correlation (-0.4208).
• Panel unit roots (Table 5): At first differences, LLC/IPS/ADF/PP reject unit root at 5% for all variables (p=0.0000), indicating I(1) stationarity after differencing.
• FEM (Table 6):
  • D(INT): -0.0040 (p=0.0046) significant negative.
  • D(LCPI): 0.9304 (p=0.0028) significant positive.
  • D(LEXR): -0.8302 (p=0.0000) significant negative.
  • D(LIPI): -0.0017 (p=0.8940) not significant.
  • R^2=0.2013; model F-stat p=0.0000.
• REM (Table 7):
  • D(INT): -0.0003 (p=0.6093) not significant.
  • D(LCPI): 0.9639 (p=0.0017) significant positive.
  • D(LEXR): -0.8356 (p=0.0000) significant negative.
  • D(LIPI): ~0 (p=0.9988) not significant.
  • R^2=0.1894; model F-stat p=0.0000.
• Hausman test (Table 8): χ2=9.623, df=4, p=0.0433; FEM preferred.
• System GMM (Table 9):
  • D(LSMI)(-1): 0.4608 (p=0.0000) significant positive persistence.
  • D(INT): -0.0005 (p=0.1530) not significant.
  • D(LCPI): 1.2903 (p=0.0000) significant positive.
  • D(LEXR): -0.5802 (p=0.0000) significant negative.
  • D(LIPI): 0.0180 (p=0.0100) significant positive.
  • S.E. of regression: 0.0014. Sargan test: 0.7370; p-value: 0.0000 (as reported). AR(1): z=-0.700, p=0.4860; AR(2): z=-10.850, p=0.0000 (as reported).
• Overall: CPI and IPI are positively and significantly associated with SMI; exchange rate depreciation (higher CCY per USD) is negatively and significantly associated; interest rate shows negative sign but is not significant in GMM. During COVID-19, notable drops in SMI coincide with early 2020 and policy responses (e.g., MCO).

The findings support the hypothesis that macroeconomic conditions significantly affect ASEAN-5 stock market performance, with differing channels:

• Interest rates: Generally negative relationship with stock indices, consistent with higher discount rates and borrowing costs reducing valuations and expected cash flows. In dynamic GMM estimates, the effect is negative but not statistically significant in the short run across the panel.
• Inflation (CPI): Positive association with stock indices in the short run, potentially reflecting pricing power in certain sectors and equities’ partial hedge properties against inflation. However, persistent inflation could raise required returns and weigh on valuations.
• Exchange rates: Depreciation of local currency (higher CCY/USD) is associated with lower stock index performance overall, consistent with cost pressures for importers and macro uncertainty outweighing potential exporter gains in the aggregate.
• Industrial production (IPI): In static models, the link is weak and insignificant; in dynamic GMM, IPI shows a small but positive and significant effect, aligning with theory that stronger real activity supports earnings and stock prices. COVID-19 disruptions (lockdowns, supply chain breaks) likely muted this channel at times. The presence of significant lagged dependence indicates momentum/persistence in stock index movements. The results underscore the importance of macro monitoring for investors and the role of stabilizing macro policies for policymakers in emerging ASEAN markets, especially during health crises.

The study contributes evidence on ASEAN-5 stock markets over 2012–2022, including the COVID-19 period, showing that CPI and IPI positively affect stock indices, while exchange rate depreciation exerts a negative effect; interest rates are generally negatively related but not robustly significant in dynamic GMM. The dynamic nature of stock index responses and macro linkages suggests both investors and policymakers should incorporate macro indicators into decision-making, particularly under pandemic shocks. The paper recommends that investors assess risk tolerance and sector exposures under changing macro conditions and that policymakers design measures to stabilize markets and support real activity during crises. Future research directions include expanding macro variables (e.g., unemployment, GDP, trade), exploring different data frequencies (weekly/quarterly), extending country coverage beyond ASEAN-5 (e.g., Vietnam, Cambodia, Myanmar), and comparing with developed markets such as the U.S., as well as longer-horizon or fully dynamic models.

• Scope limited to ASEAN-5; results may not generalize to other regions.
• Short-run focus using monthly data; long-run dynamics and structural breaks require further study.
• Variable set restricted to INT, CPI, EXR, and IPI; omission of other macro factors (e.g., unemployment, GDP, trade) may leave residual confounding.
• Data collection constraints inherent to secondary sources and monthly frequency during the recent pandemic period.
• The reported dynamic panel diagnostics include an AR(2) test with p=0.0000 (as presented), which may indicate residual autocorrelation; instrument validity and specification merits further robustness checks in future work.