1. INTRODUCTION:

Interconnectedness among different economic and financial indicators of the economy has a crucial role in the estimation and management of risk. Various research studies have demonstrated that factors like interest rate, inflation, money supply, commodity prices, exchange rate, etc., remarkably affect the movement of stock prices.

Studying the interconnectedness among such indicators can help in judging the overall economic and financial stability of the nation. A comprehensive evaluation of the interconnections may help in estimating how the shocks from one part of the system get transferred to another part. Such evaluation may provide valuable insights to different stakeholders (like investors, policymakers, financial institutes, portfolio managers, etc.) to help them develop optimum risk management strategies.

In recent years, relationship shared by macroeconomic indicators with stock markets has been intensely studied by various researchers of different emerging and developed nations^1-9.

Most of the studies have confirmed the existence of significant connections between the studied macroeconomic factors and the stock market. While extensive studies have discovered these interconnections at global levels, less consideration has been given to the Indian economy in recent years.

Indian stock market is one of the fastest growing markets in the world, and it is gaining the attention of investors from different parts of the world. It has undergone enormous development not only in terms of capitalization but also in terms of volume of trade and number of investors. Due to its rapid growth and increasing appeal to global investors, exploring the dynamics of association shared by the Indian stock market with macroeconomic indicators becomes essential. It may further guide international investors in developing proper strategies for fund allocation.

This study attempts to explore the interconnections between the Indian stock market and different macroeconomic variables. The interconnections of BSESENSEX have been explored with different macroeconomic determinants (namely crude oil prices, exchange rate, foreign institutional investment, gold prices, index of industrial production, and wholesale price index).

2. LITERATURE REVIEW:

The existing literature reveals a remarkable association between the stock market and different macroeconomic determinants across different developed as well as emerging nations. Studies have explored such interconnections not only with reference to developed economies but emerging nations have also been studied by the researchers.

Researchers have found positive connections between GDP (gross domestic product) and BSE Sensex. It was also revealed that the stock values are negatively affected by the rates of interest and inflation in the economy.

Researchers have identified positive interconnections between GDP and stock markets. On the other hand, interest rates and inflation have been observed to be negatively impacting the stock markets. Studies related to different nations like Korea, Ghana, South Africa, and others have demonstrated diverse associations between stock markets and macroeconomic determinants. However, with reference to the Indian economy expressly, mixed outcomes have been noted, which suggests the need for further investigation.

2.1 Global stock market and macroeconomic indicators:

Kwon and Shin¹⁰ employed different statistical tests for studying the association between stock prices and the macroeconomic indicators of Korea. The stock prices were found to be integrated with some of the studied economic variables like exchange rate, money supply, trade balance, etc. It was also revealed that the stock price does not lead to the economic variables of the Korean economy. Hardouvelis¹¹ analyzed the reaction of stock prices to the announcements of various macroeconomic variables. The study revealed that the stock prices substantially react to the monetary variables. The studied stocks were found to be most sensitive to the monetary news. The findings suggested that the observed reaction of the stock prices is due to the implied market perception that the Federal Reserves are critical in future macroeconomic developments. Kyereboah and Agyire¹² used a cointegration test to examine the impact of different macroeconomic indicators on the Ghana stock market from 1991 to 2005. The study found that lending rates and inflation rates in the economy negatively affect Ghana's stock market performance.

Megaravalli and Sampagnaro¹³ examined the linkages of Indian, Chinese, and Japanese stock markets with significant macroeconomic variables from 2008 to 2016. The stock markets are positively associated with the exchange rate. On the other hand, it was found to be negatively associated with inflation. It was also revealed that no short-term relationship exists between stock markets and the variables studied.

Hsing¹⁴ examined the impact of macroeconomic indicators on South Africa's stock market. The study found the stock market to be positively associated with some of the studied variables only (GDP growth, ratio of money supply to GDP, and stock index of the U.S.). However, negative connections were observed with other variables (the ratio of the government deficit to GDP, the domestic real interest rate, the nominal effective exchange rate, the domestic inflation rate, and the U.S. government bond yield). Barakat et al.¹⁵ studied the relationship shared by the stock markets of Egypt and Tunisia with different macroeconomic indicators from 1998 to 2014. The study found causal and integrated connections between the stock markets and some variables like money supply, interest rate, and exchange rate. However, the consumer price index (CPI) was found to be associated only with Egypt's stock market.

Jareno et al.¹⁶ examined the associations shared by the stock markets of major economies (Germany, Italy, Spain, France, UK, and the U.S.) with different macroeconomic factors (gross domestic product, the consumer price index, the industrial production index, and the unemployment). The correlation analysis revealed a remarkable association between the stock markets of selected nations and the studied variables. Agwu and Haydar ¹⁷ studied the impact of different macroeconomic factors on London's stock market. Based on the multiple regression method, the study found a significant impact of the studied variables on the London Stock Exchange.

2.2 Indian stock market and macroeconomic indicators:

Gautam and Goyal¹⁸ applied a regression model to analyze the impact of BSE Sensex on different macroeconomic variables of the Indian economy. The study found BSE Sensex to be significantly associated with gold and IIP (Index of Industrial Production). However, no such connection could be observed between FII (Foreign Institutional Investment) and inflation. Singh¹⁹ used correlation, regression, and causality techniques to study the impact of different macroeconomic factors on the Indian stock market. The study found a causal connection between FII (Foreign Institutional Investment) and the stock market. Moreover, the exchange rate was found to be negatively impacting the stock market. It was also revealed that whenever the rupee value depreciates, the stock values also go down.

Lairellakpam and Dash²⁰ used different econometrics tests to explore the macroeconomic factors affecting the volatility of the Indian stock market. The study found that factors like the rate of interest and gold and oil prices do not significantly impact the volatility of the stock market. However, the movement of the exchange rate was found to be significantly impacted by the stock market. Dasgupta²¹ used different econometrics tests to study the relationships shared by BSE Sensex and different macroeconomic variables from 2007 to 2012. The study found no causal connection between BSE Sensex and the studied variables. However, it was found to be integrated with some of the variables like IIP (index of industrial production) and call money rate.

Furthermore, the study revealed that there needs to be more informational efficiency in India's stock market. Reddy²² used regression to study the impact of Gross Domestic Product (GDP), rate of interest, and inflation on the stock prices of selected Indian companies from 1997 to 2009. The study found that the movement of stock prices is inversely related to the rate of interest and inflation in the economy. However, GDP growth was found to be positively affecting the stock prices. The study recommended that the government introduce policies that may lead to a reduced inflation rate and improved living standards. Moreover, policymakers were also advised to moderate interest rates and promote investments.

Kapoor et al.²³ applied different tests to study the relationship shared by India's stock market and some major economies of the world. The study considered the stock markets of London, New York, Australia, and India. The considered stock markets were found to be significantly interlinked with each other. Moreover, the study revealed that the considered stock markets are fundamentally different from each other. Shang and Hamori²⁴ investigated the interactions of the stock markets of the U.S. (United States) and E.U. (European Union) with different macroeconomic variables. The study identified that the source of systematic risk varies across different periods and market conditions. Crude oil was found to be a consistent shock transmitter across the studied markets. Giri and Joshi²⁵ adopted ARDL bounds and the VECM method to examine the association between stock prices and India's macroeconomic indicators. The findings confirmed the existence of significant integrating and causal connections among the studied variables. Stock prices were found to be significantly influenced by economic growth, inflation, and exchange rates. On the other hand, it was found to be negatively affected by crude oil prices.

3. RESEARCH METHODOLOGY:

The study is investigative and utilizes secondary data solely. The data was collected from various official websites, such as BSE (Bombay Stock Exchange), SEBI (Securities Exchange Board of India), RBI (Reserve Bank of India), World Bank, and others. Ten years of monthly data ranging from 2013 to 2023 has been considered. Different statistical tests like correlation, least squares regression, Granger Causality, and cointegration have been utilized to study the impact of the selected macroeconomic variables on the Indian stock market. Table 1 provides the details of the variables considered for the study.

Table 1: Variables considered

Variable	Sign.	Dependent or Independent
Bombay Stock Exchange Sensex	BSE SENSEX	Dependent Variable
Crude Oil Prices	CP	Independent Variable
Exchange Rate (USD/INR)	ER	Independent Variable
Foreign Institutional Investment	FII	Independent Variable
Gold Prices	GP	Independent Variable
Index of Industrial Production	IIP	Independent Variable
Wholesale Price Index	WPI	Independent Variable

Source: Authors’ Computation

3.1 Variables Description:

1. BSE Sensex: It is the benchmark index of India's Bombay Stock Exchange, representing the performances of the top 30 largest traded stocks listed in BSE.

2. Crude Oil Prices (C.P.): Crude oil is one of the vital commodities traded worldwide. Being one of the essential commodities, its prices affect other industries as well (including transportation, manufacturing, and energy production.

3. Exchange Rate (E.R.): The rates of exchange of currencies significantly impact global trade, investments, and economic policies. The exchange rate between U.S. dollar and Indian rupee represents how much amount of the rupee can be bought in exchange for one dollar.

4. Foreign Institutional Investment (FII): It indicates the funds invested by foreign institutional investors in the financial markets of a nation. These investments remarkably impact the economic and financial stability of a nation.

5. Gold Prices (G.P.): Gold is also one of the most preferred commodities for people to invest their funds. As it is one of the highest trading commodities, gold prices play a significant role in affecting the price movements of other commodities as well. Moreover, market sentiments and investors' decisions are also significantly affected by gold price fluctuations.

6. Index of Industrial Production (IIP): it is one of the critical economic indicators measuring the output of different industries of an economy for a particular period. It represents the growth and advancements of critical industrial sectors of a nation.

7. Wholesale Price Index (WPI): This index represents the movements in the wholesale prices of various goods traded in an economy. It comprises the wholesale prices of various goods like food items, fuels, metals, chemicals, and others. Financial specialists and economists use this index to monitor inflation trends.

4. DATA ANALYSIS:

For examining the degree of association shared by the selected variables, correlation test has been applied. Table 2 reports the results of the correlation tests.

Table 2: Correlation Test Results

	BSE SENSEX	CP	ER	FII	GP	IIP	WPI
BSE SENSEX	1.000	0.427	0.900	0.346	0.818	0.193	0.246
CP	0.427	1.000	0.478	0.123	0.129	0.241	0.189
ER	0.900	0.478	1.000	0.314	0.758	0.009	0.014
FII	0.346	0.123	0.314	1.000	0.417	0.007	0.160
GP	0.818	0.129	0.758	0.417	1.000	-0.010	0.139
IIP	0.193	0.241	0.009	0.007	-0.010	1.000	0.719
WPI	0.246	0.189	0.014	0.160	0.139	0.719	1.000

Source: Authors’ Computation

The correlation test revealed that the studied variables are significantly interrelated with each other. The level of correlation was found to be varied for different variables. It suggests that the studied variables are interconnected with each other; however, the degree of association is not consistent among all the variables.

BSE Sensex was found to be having strong positive connections with E.R. (0.900) and G.P. (0.818). It suggested that any fluctuation in the exchange rate and gold prices significantly affects the stock price movements. However, with C.P. and FII, it was found to be sharing moderately positive connections only, i.e., 0.427 and 0.346. It indicated that these variables exert some level of influence on the stock price movements. G.P. was observed to have strong positive correlations with both BSE Sensex (0.818) and E.R. (0.758). It suggests that gold price movements react remarkably to the prevailing economic and market conditions. The IIP was found to have a moderately positive correlation with WPI (0.719). It highlighted the presence of interconnectedness between industrial outcomes and prices. These findings help investors, policymakers, and academicians who seek to understand the dynamics of connections between economic indicators and the financial market.

The least square regression was adopted to analyze the relationship among the studied variables further. Table 3 shows the results of least squares regression. The impact of the other variables considered for BSESENSEX has been explored.

Table 3: Least Squares Regression Analysis

Variable	Coefficient	Std. Error	t-Statistic	Prob.
CP	0.107	0.140	0.768	0.444
ER	1277.797	112.067	11.402	0.000
FII	-0.010	0.029	-0.339	0.735
GP	14.780	2.585	5.717	0.000
IIP	184.324	83.716	2.202	0.030
WPI	206.700	74.937	2.758	0.007
C	-74555.170	4794.867	-15.549	0.000
R-squared	0.897	Mean dependent var		37960.120
Adjusted R-squared	0.892	S.D. dependent var		14190.720
S.E. of regression	4665.156	Akaike info criterion		19.785
Sum squared resid	2720000000.000	Schwarz criterion		19.938
Log likelihood	-1298.823	Hannan-Quinn criter.		19.847
F-statistic	181.188	Durbin-Watson stat		0.603
Prob(F-statistic)	0.000

Source: Authors’ Computation

E.R. and G.P. were observed to have a strong positive influence on BSESENSEX as these coefficients were found to be significant, i.e., 1277.797 and 14.780, respectively. It implies that variations in exchange rates and gold prices substantially impact the movement trends of BSE SENSEX. However, with other variables like C.P., FII, and WPI, no such strong connections could be observed. These variables were observed to exert only weak or negligible impacts on BSE Sensex. The value of adjusted R-square and F-statistic was observed to be 0.892, which suggests that the model is a good fit.

The Granger causality test was employed to explore the causal connections between the studied variables. This test helps in understanding the potential causal connections along with their directions among the studied variables. Table 4 presents the results of the Granger causality test.

Table 4: Granger Causality Test Results

Null Hypothesis:	F-Statistic	Prob.
CP does not Granger Cause BSE SENSEX	1.043	0.355
BSE SENSEX does not Granger Cause CP	0.955	0.388
ER does not Granger Cause BSE SENSEX	3.646	0.029
BSE SENSEX does not Granger Cause ER	1.643	0.198
FII does not Granger Cause BSE SENSEX	2.827	0.063
BSE SENSEX does not Granger Cause FII	3.784	0.025
GP does not Granger Cause BSE SENSEX	0.686	0.505
BSE SENSEX does not Granger Cause GP	0.304	0.739
IIP does not Granger Cause BSE SENSEX	22.438	0.000
BSE SENSEX does not Granger Cause IIP	1.075	0.345
WPI does not Granger Cause BSE SENSEX	7.214	0.001
BSE SENSEX does not Granger Cause WPI	1.102	0.336
ER does not Granger Cause CP	1.272	0.284
CP does not Granger Cause ER	0.051	0.950
FII does not Granger Cause CP	1.718	0.184
CP does not Granger Cause FII	1.092	0.339
GP does not Granger Cause CP	0.344	0.710
CP does not Granger Cause GP	0.908	0.406
IIP does not Granger Cause CP	4.430	0.014
CP does not Granger Cause IIP	0.853	0.428
WPI does not Granger Cause CP	5.189	0.007
CP does not Granger Cause WPI	0.246	0.783
FII does not Granger Cause ER	0.174	0.841
ER does not Granger Cause FII	2.456	0.090
GP does not Granger Cause ER	0.825	0.441
ER does not Granger Cause GP	2.453	0.090
IIP does not Granger Cause ER	4.077	0.019
ER does not Granger Cause IIP	0.148	0.863
WPI does not Granger Cause ER	0.360	0.698
ER does not Granger Cause WPI	0.065	0.937
GP does not Granger Cause FII	4.866	0.009
FII does not Granger Cause GP	0.285	0.753
IIP does not Granger Cause FII	4.070	0.019
FII does not Granger Cause IIP	0.056	0.946
WPI does not Granger Cause FII	0.093	0.911
FII does not Granger Cause WPI	1.342	0.265
IIP does not Granger Cause GP	1.363	0.260
GP does not Granger Cause IIP	0.262	0.770
WPI does not Granger Cause GP	2.140	0.122
GP does not Granger Cause WPI	0.112	0.894
WPI does not Granger Cause IIP	11.574	0.000
IIP does not Granger Cause WPI	0.626	0.537

Source: Authors’ Computation

Mixed outcomes were obtained regarding the causal interconnections shared by the BSE Sensex with other variables. With some variables, unidirectional or bidirectional causality was observed. However, with other variables, no such connections could be observed. With variables like C.P., E.R., IIP, and WPI, bidirectional causality was noted. Unidirectional causality was observed between FII and BSE Sensex, which implied that FII affects stock price movements but not vice versa. However, no causal connections were observed between BSE Sensex and G.P., which showed null causal effects of these variables. Similarly, no consistent results were obtained for the other studied variables.

Before proceeding to further analysis, the stationarity of the series of studied variables has been checked. The ADF (Augmented Dicky Fuller) and P.P. (Phillips Perron) test has been used. Table 5 shows the results of ADF unit root test.

Table 5: Results of ADF Unit Root Test at level and at first difference of series of studied variables

	ADF Test Statistic
Markets (Index)	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
	Intercept	Intercept	Trend and Intercept	Trend and Intercept	None	None
BSE SENSEX	-2.228	-11.796*	-2.877	-11.838*	-3.179	-11.379*
CP	-1.426	-11.201*	-2.029	-11.176*	-1.178	-11.219*
ER	-0.547	-12.041*	-2.810	-12.001*	-2.236	-11.629*
FII	-8.316*	-11.943*	-9.157*	-11.898*	-3.653*	-11.991*
GP	-1.987	-11.624*	-0.822	-11.876*	-0.930	-11.645*
IIP	-5.822*	-10.811*	-5.806*	-10.771*	-5.489*	-10.853*
WPI	-4.663*	-10.331*	-4.676*	-10.289*	-4.265*	-10.371*

Source: Authors’ Computation

Mixed results were found. Only some variables were found stationary at levels. However, others were found stationary at first difference. Variables like FII, IIP, and WPI were observed to be integrated at level, i.e., I(0). On the other hand, other variables (namely BSE Sensex, CP, E.R., G.P.) were found to be integrated at the first difference, i.e., I(1). In such conditions, where all variables are not integrated in the same order, the ARDL (Autoregressive Distributed Lag) test of cointegration can be applied.

Table 6 reports the results of P.P. test. Similar results were obtained, and it has been confirmed that some series of all the variables are not stationary at the same levels.

Table 6: Results of PP Unit Root Test Results at level and at first difference of series of studied variables

	PP Test Statistic
Markets (Index)	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
	Intercept	Intercept	Trend and Intercept	Trend and Intercept	None	None
BSE SENSEX	-2.362	-11.947*	-2.891	-12.101*	-3.644	-11.387*
CP	-1.426	-11.204*	-2.065	-11.177*	-1.174	-11.221*
ER	-0.547	-12.041*	-2.949	-12.001*	-2.302	-11.627*
FII	-8.711*	-91.485*	-9.224*	-92.806*	-6.496*	-94.135*
GP	-1.981	-11.624*	-0.822	-11.901*	-0.930	-11.645*
IIP	-5.785*	-23.917*	-5.771*	-23.771*	-5.411*	-24.044*
WPI	-4.574*	-30.511*	-4.578*	-30.464*	-4.158*	-30.784*

Source: Authors’ Computation

Notes: Significance at *1 per cent; the lag lengths included in the models are based on the AIC; the tests of ADF and PP are based on constant and trend; the 1 per cent critical values are based on MacKinnon (1996)

Thus, both unit root test results confirmed that the ARDL cointegration test can be adopted for further analysis. This test has been applied to know the long-term interconnections among the studied variables. It provides insights into the relationship shared by the dependent variable with other independent variables as well as with its own lagged values. Table 7 shows the results of ARDL cointegration test.

Table 7: ARDL Cointegration Test Results

Variable	Coefficient
BSE_SENSEX (-1)	0.947159
CP__CRUDE_OIL_PRICES_	0.016096
ER_USD_INR_	-480.779
ER_USD_INR_(-1)	563.6824
FII	-0.00663
FII(-1)	0.01656
GP_GOLD_PRICES_	-0.51379
IIP__INDEX_OF_INDUSTRIAL_PRODUCTION_	-14.7545
IIP__INDEX_OF_INDUSTRIAL_PRODUCTION_(-1)	97.18276
IIP__INDEX_OF_INDUSTRIAL_PRODUCTION_(-2)	-98.675
WPI_WHOLESALE_PRICE_INDEX_	15.13247
C	-3705.78
Model Fitness
R-squared	0.990581
Adjusted R-squared	0.989703
F-statistic	1128.155
Durbin-Watson stat	1.957084

Source: Authors’ Computation

Notes: Maximum dependent lags: 4 (Automatic selection). Model selection method: Akaike info criterion (AIC). Dependent Variable: BSE SENSEX. Dynamic regressors (4 lags, automatic): CP, ER, FII, GP, IIP, WPI. Fixed regressors: C, TREND. Number of models evaluated: 62500. Finally selected model using AIC method: ARDL (1, 0, 1, 1, 0, 2, and 0). * are statistically significant coefficients @5% alpha level with p-values below 0.05 and t > 1.96.

The coefficients of each variable represented the impacts inserted by a unit change in the variable on the dependent variable. The results suggested remarkable influence of the studied variables on BSE Sensex. Apart from the other independent variables, BSE Sensex was observed to be significantly impacted by its previous values as well. The observed value of the coefficient indicated that a rise in the lagged value of BSE Sensex by one unit leads to a 0.947 unit rise in its current value. Overall, the outcomes indicated the presence of significant linkages between BSE Sensex and other considered variables.

This analysis helps in knowing the magnitude of the changes brought by changes in the independent variables on the dependent variable. The value of R-squared demonstrated that independent variables explain 99.1% variation of BSE Sensex. The F statistics and Durbin-Watson statistics also demonstrated the fitness of the model. Overall, high R- R-squared value, statistically significant coefficients, and other statistics indicated that the employed model is statistically fit for the studied variables. It also offers valuable insights into the interconnections between BSE Sensex and other studied variables.

5. DISCUSSION AND CONCLUSION:

The current study investigated the short and long-term interconnections shared by BSE Sensex with other macroeconomic indicators (namely C.P., E.R., FII, G.P., IIP, and WPI). Monthly data ranging from 2013 to 2023 has been analyzed with the help of different statistical tools like Correlation, Least Squares Regression, Granger Causality, Unit Root Tests, and Cointegration tests. Indian stock market and its various indicators have also been studied by researchers like Saji and Harikumar²⁶, Pramod et al.²⁷, Khan²⁸, Chadha²⁹, Susruth³⁰, Manu et al.³¹, Arora³², Kaushik³³, Khanna³⁴, Savita et al.³⁵

The study is investigative and utilizes secondary data solely. The data was collected from various official websites, such as BSE (Bombay Stock Exchange), SEBI (Securities Exchange Board of India), RBI (Reserve Bank of India), World Bank, and others. Ten years of monthly data ranging from 2013 to 2023 has been considered. Different statistical tests like correlation, least squares regression, Granger Causality, unit root tests, and cointegration tests have been utilized to examine the impact of the selected macroeconomic variables on the Indian stock market. Table provides the details of the variables considered for the study.

The correlation test revealed significant interconnections among the studied variables. However, the level of interconnections was observed to be varying with each variable. The regression results indicated mixed outcomes. Variables like E.R. and G.P. were observed to be strongly impacting the BSE Sensex. However, other variables could only insert a minor influence.

Similarly, the Granger causality test also revealed mixed outcomes with different variables. Most of the studied variables were found to be causally connected with BSE Sensex. The ARDL cointegration test also suggested that BSE Sensex shares long-run interconnections with other studied variables.

Overall, the findings revealed the complex and varied impact of the studied macroeconomic indicators on the Indian stock market. Some variables had strong interconnections, while some had only weak interconnections with BSE Sensex. This implies that macroeconomic indicators remarkably influence the performance of the Indian stock market. However, not all macroeconomic indicators impose similar influences.

The findings highlight the intricate and varied effects of macroeconomic information on the Indian stock market, specifically with regard to the BSE Sensex. It's probable that some macroeconomic factors have a significant impact on stock market performance and even drive market trends and investment decisions given how closely some variables are tied to one another. However, the weak correlations between the other variables point to a lower level of consistency or influence. Market analysis requires a comprehensive approach since various macroeconomic data impact the stock market in different ways. Market analysis requires a comprehensive approach since various macroeconomic data impact the stock market in different ways. It is vital to carefully choose and concentrate on macroeconomic indicators with greater linkages in order to accomplish more accurate forecasting and strategic planning. This variation emphasizes how crucial this strategy is. Researchers, academicians, investors, and policymakers may gain beneficial insights from the study's findings for effective decision-making and strategy formulation. Further research is required to delve deeper into the dynamic relationship and enhance the understanding of the behavior of the Indian stock market.

6. CONFLICT OF INTEREST:

The author declares no conflict of interest.

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Received on 20.05.2025 Revised on 25.06.2025

Accepted on 22.07.2025 Published on 14.11.2025

Available online from November 25, 2025

Int. J. of Reviews and Res. in Social Sci. 2025; 13(4):203-211.

DOI: 10.52711/2454-2687.2025.00029

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