Impact of Macroeconomic Factors on Selected Sectors of BSE

The liberalization of the Indian Economy in 1991 ushered the Indian stock markets into a new era. Stock markets became an important barometer for measuring the economic health of India. The relationship between stock markets and macroeconomic variables has been widely studied with varying results. This study explores the relationship between selected macroeconomic variables and various BSE indices, using correlation and regression analysis to understand the effect of macroeconomic variables on Bombay Stock Exchange indices.

Dasgupta studied the relationship between certain macroeconomic variables (WPI, IIP, call money rates, exchange rates) and BSE Sensex, finding long-run relationships: IIP and interest rate positively related, WPI and exchange rate negatively related, with no short-run relationship. Sharma and Mahendru considered exchange rate, forex reserves, gold price, and inflation rate; exchange rate and gold prices significantly affected stock prices, while forex reserves and inflation did not. Chen et al. examined efficient market hypothesis and rational expectations, finding industrial production, yield curve, and risk premium changes significantly explain stock price variability; the stock market index was an insignificant independent variable. Naka et al. used VECM to analyze Indian stock markets and macroeconomic variables, finding GDP growth positively and inflation negatively affecting stock market performance. Nieh and Lee found no significant relationship between exchange rates and stock market performance for G-7 countries. Garg and Kalra found inflation and unemployment negatively related to Indian stock prices, while exchange rate, forex reserves, gold price, and GDP were positively related. Cook used threshold autoregressive cointegration revealing asymmetries in relationships. Bose suggested bilateral causality between equity market development and economic growth. Sangmi and Hassan examined six macroeconomic variables on Sensex, Nifty, and BSE 100 using monthly data and multiple regression, finding significant relationships between indices and macroeconomic variables.

The study examines the impact of seven macroeconomic variables on selected BSE sectoral indices using monthly data from April 2014 to March 2019. Macroeconomic variables: Call Money Rate (CMR), Crude Oil Prices, Exchange Rate (Dollar Price), Consumer Price Index (CPI), Foreign Institutional Investors (FIIs), Index of Industrial Production (IIP), and Gold Price. BSE indices analyzed: SENSEX, Metals, Auto, Capital Goods, Fast Moving Consumer Goods (FMCG), and Consumer Durables; detailed regression results are presented for Auto, BANKEX, Consumer Durables, FMCG, and Metal. Data source for macroeconomic factors: Reserve Bank of India database (http://dbie.rbi.org.in/). Hypotheses: H0: No significant relationship between BSE indices and selected macroeconomic variables; H1: Significant relationship exists. Statistical techniques: Descriptive statistics (mean, median, max, min, SD, skewness, kurtosis), Karl Pearson’s correlation coefficients, and multiple linear regression for each index with the seven macro variables as predictors. Model form: Y = α + β1 IIP + β2 Gold + β3 Dollar + β4 Crude Oil + β5 FIIs + β6 CPI + β7 CMR + u. Significance level: 5%.

• Descriptive statistics indicate non-normality (kurtosis deviating from 3) and asymmetry across variables; several series exhibit negative skewness (e.g., BSE Metal, Gold).
• Correlation patterns (selected): Indices are highly inter-correlated. IIP is positively correlated with indices (e.g., 0.80 with FMCG, 0.85 with Consumer Durables). CPI and CMR show high negative correlations with indices (e.g., CMR with Auto −0.78, with CD −0.74, with FMCG −0.68). Crude oil shows low correlations with most indices; Dollar shows moderate positive correlations with indices in the correlation matrix.
• Regression: All models exhibit strong fit and significant overall F-tests. • BSE Auto: Multiple R = 0.865; Adjusted R² = 0.715; F = 22.13 (p < 1e-12). Significant predictors: Dollar (β = −428.98, p = 0.00065), CPI (β = −415.16, p = 0.0415), CMR (β = −3956.80, p ≈ 8.9e-10). Non-significant: IIP, Gold, Crude Oil, FIIs. • BSE BANKEX: Multiple R = 0.897; Adjusted R² = 0.779; F = 30.64 (p ≈ 2.57e-16). Significant: IIP (β = 220.73, p = 0.00035), Crude Oil (β = 1.224, p = 0.00163), CPI (β = −1354.47, p ≈ 6.19e-06), CMR (β = −1855.51, p = 0.0126). Non-significant: Gold, Dollar, FIIs. • BSE Consumer Durables: Multiple R = 0.915; Adjusted R² = 0.815; F = 38.11 (p ≈ 2.67e-18). Significant: IIP (β = 289.30, p ≈ 1.3e-06), Crude Oil (β = 1.2155, p = 0.000712), CMR (β = −2729.47, p = 0.000126). CPI marginal (p = 0.052). Non-significant: Gold, Dollar, FIIs. • BSE FMCG: Multiple R = 0.909; Adjusted R² = 0.802; F = 35.22 (p ≈ 1.42e-17). Significant: IIP (β = 60.02, p = 0.00214), Crude Oil (β = 0.5258, p ≈ 4.83e-05), CPI (β = −347.77, p = 0.000195), CMR (β = −693.94, p = 0.00414). Non-significant: Gold, Dollar, FIIs. • BSE Metal: Multiple R = 0.904; Adjusted R² = 0.793; F = 33.32 (p ≈ 4.56e-17). Significant: IIP (β = 61.01, p = 0.0266), Dollar (β = −433.10, p ≈ 3.55e-07), Crude Oil (β = 1.469, p ≈ 1.42e-11), CPI (β = −373.56, p = 0.00422), CMR (β = −1779.58, p ≈ 2.03e-06). Non-significant: Gold, FIIs.
• Cross-sector pattern: Call Money Rate (CMR) is the only variable that consistently and significantly affects all indices, with a negative sign. IIP is positively significant for BANKEX, CD, FMCG, and Metal. Crude oil is positively significant in BANKEX, CD, FMCG, and Metal. CPI generally exerts a significant negative effect. Dollar (exchange rate) significantly negatively affects Auto and Metal. Gold price and FIIs are generally not significant across sectors.

The study set out to determine whether external macroeconomic variables significantly influence BSE sectoral indices. The results demonstrate that macroeconomic conditions are strongly associated with sectoral equity performance. The consistent, significant, and negative effect of Call Money Rate (CMR) across all indices indicates that tighter domestic liquidity/interest conditions depress sectoral equity prices broadly. Real activity, proxied by IIP, supports sectors such as banking, consumer durables, FMCG, and metals, highlighting the sensitivity of these sectors to domestic industrial output. Crude oil prices, while often considered a cost factor, display positive associations with several indices, suggesting that during the sample period higher oil prices coincided with broader macro strength benefiting these sectors. Inflation pressure (CPI) negatively affects indices, reflecting valuation compression or cost pressures. Exchange rate depreciation (higher Dollar price) exerts negative pressure on Auto and Metal, likely via import cost and commodity pricing channels. Gold prices and FIIs do not show robust explanatory power in this sample. Overall, the findings confirm that domestic macroeconomic variables, particularly monetary conditions, industrial activity, and inflation, play a substantial role in explaining sectoral equity movements at the BSE.

Using monthly data from April 2014 to March 2019, the study employed correlation and multiple regression to assess the impact of seven macroeconomic variables on five BSE sectoral indices. Three macro variables emerge as relatively more influential: Call Money Rate (negative across all sectors), Index of Industrial Production (positive for BANKEX, FMCG, Consumer Durables, and Metal), and Crude Oil (positive for BANKEX, FMCG, Consumer Durables, and Metal). CPI generally exhibits a negative relationship, while Dollar (exchange rate) negatively affects Auto and Metal. Gold price and FIIs largely lack significance. The study concludes that domestic macroeconomic factors play a relatively larger role in influencing BSE indices. Potential future research could extend the time horizon, include additional sectors and macro variables, examine non-linear dynamics, and apply alternative econometric frameworks (e.g., VECM, threshold models) to assess robustness.