A causality investigation into stock prices and macroeconomic indicators in the Indian stock market

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Stock prices are closely linked to macroeconomic indicators, a crucial aspect for investors, policymakers, and researchers in emerging markets like India, influencing investment decisions and policy formulation. Methods The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices from April 2009 to March 2023. Results The outcomes of the research suggest that macroeconomic variables exert influence on the Indian stock market, across the short and long term. Moreover, the results of the paired Granger causality test suggest that the domestic macroeconomic variables possess predictive significance for stock prices in the Indian stock market. Conclusion The study reveals that macroeconomic variables significantly impact the Indian stock market, highlighting the need for investors and portfolio managers to monitor these conditions to optimize returns and mitigate risks. The Reserve Bank of India should maintain an optimal money supply to prevent inflation and exchange rate fluctuations, while bolstering the export sector and facilitating imports through initiatives like Atma-nirbhar Bharat Abhiyan and Make in India. Policies focusing on productivity, infrastructure, and a favourable business environment are also crucial. Therefore, it is crucial for investors and portfolio managers to consistently analyse the current macroeconomic conditions in order to maximize their profits and minimize risks. This research has extensive significance for comprehending the intricate connections between the stock market and macroeconomic issues. 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F1000Research 2025, 13 :1299 ( https://doi.org/10.12688/f1000research.157041.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] Sanjay Singh Chauhan 1 , Pradeep Suri 1 , Firoz Alam 2 , Umme Hani 2 , Amar Johri 2 , Farman Ali https://orcid.org/0000-0002-3364-2355 1 Sanjay Singh Chauhan 1 , Pradeep Suri 1 , [...] Firoz Alam 2 , Umme Hani 2 , Amar Johri 2 , Farman Ali https://orcid.org/0000-0002-3364-2355 1 PUBLISHED 03 Jun 2025 Author details Author details 1 Management, Uttaranchal University, Dehradun, Uttarakhand, 248007, India 2 College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh, 11673, Saudi Arabia Sanjay Singh Chauhan Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software Pradeep Suri Roles: Supervision, Validation Firoz Alam Roles: Investigation, Writing – Original Draft Preparation Umme Hani Roles: Conceptualization, Formal Analysis Amar Johri Roles: Methodology, Visualization, Writing – Original Draft Preparation Farman Ali Roles: Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Uttaranchal University gateway. Abstract Background of the study The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. Stock prices are closely linked to macroeconomic indicators, a crucial aspect for investors, policymakers, and researchers in emerging markets like India, influencing investment decisions and policy formulation. Methods The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices from April 2009 to March 2023. Results The outcomes of the research suggest that macroeconomic variables exert influence on the Indian stock market, across the short and long term. Moreover, the results of the paired Granger causality test suggest that the domestic macroeconomic variables possess predictive significance for stock prices in the Indian stock market. Conclusion The study reveals that macroeconomic variables significantly impact the Indian stock market, highlighting the need for investors and portfolio managers to monitor these conditions to optimize returns and mitigate risks. The Reserve Bank of India should maintain an optimal money supply to prevent inflation and exchange rate fluctuations, while bolstering the export sector and facilitating imports through initiatives like Atma-nirbhar Bharat Abhiyan and Make in India. Policies focusing on productivity, infrastructure, and a favourable business environment are also crucial. Therefore, it is crucial for investors and portfolio managers to consistently analyse the current macroeconomic conditions in order to maximize their profits and minimize risks. This research has extensive significance for comprehending the intricate connections between the stock market and macroeconomic issues. READ ALL READ LESS Keywords Indian Stock markets, Macroeconomic variables, Causal relationships, Stock prices, ARDL Corresponding Author(s) Sanjay Singh Chauhan ( [email protected] ) Close Corresponding author: Sanjay Singh Chauhan Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Singh Chauhan S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Singh Chauhan S, Suri P, Alam F et al. A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.12688/f1000research.157041.3 ) First published: 30 Oct 2024, 13 :1299 ( https://doi.org/10.12688/f1000research.157041.1 ) Latest published: 03 Jun 2025, 13 :1299 ( https://doi.org/10.12688/f1000research.157041.3 ) Revised Amendments from Version 2 Recent studies have been incorporated into the literature review. In addition to this, the theoretical contributions of the study in added. The new version includes research gap and how the gap is filled. Furthermore, a detailed justification has been provided for the selection of the data period from 2009 to 2023, emphasizing its relevance and significance. It also includes a comprehensive discussion on the importance of research focused on the stock market, underlining its implications for investors, policymakers, and the overall economy. Recent studies have been incorporated into the literature review. In addition to this, the theoretical contributions of the study in added. The new version includes research gap and how the gap is filled. Furthermore, a detailed justification has been provided for the selection of the data period from 2009 to 2023, emphasizing its relevance and significance. It also includes a comprehensive discussion on the importance of research focused on the stock market, underlining its implications for investors, policymakers, and the overall economy. See the authors' detailed response to the review by Hazman Samsudin See the authors' detailed response to the review by Baranidharan Subburayan See the authors' detailed response to the review by Alireza Askarzadeh See the authors' detailed response to the review by Aamir Aijaz Syed READ REVIEWER RESPONSES Introduction The Indian economy is ranked tenth in terms of nominal gross domestic product (GDP), and the International Monetary Fund (IMF) reports that it is the third largest economy in the world in terms of purchasing power parity (PPP). Kumar et al. (2023) have observed a significant increase in the quantity and volume of investments made in the Indian stock market both domestic and foreign investors in the past two decades. One of the key elements of an economy is the stock market. Its significance lies in its ability to propel a nation’s economy forward. The stock market serves as a gauge of the health of the economy and a vehicle for capital formation. An efficient economy can be inferred from the presence of a robust banking system and a stock market that is consistently increasing in value ( Kumari, 2011 ). The two primary functions of the stock markets are aiding in price discovery and offering liquidity. They facilitate firms in undertaking large-scale initiatives by enhancing the primary market. A strong stock market helps to stimulate the economy by facilitating efficient growth and saving, wise investment allocation, and the draw of foreign direct investment (FDI). Nevertheless, for the stock market to fulfil its vital role, it is imperative that it maintains a robust interaction with macroeconomic forces ( Tripathi, V., & Seth, R. 2014 ). The study on stock market is important as it plays crucial role in economic stability, financial forecasting and investment decision making. Stock market volatility is a key element in asset pricing and portfolio management, making it crucial for investors and policymakers. A study conducted by Dai et al. (2020) revealed that stock market implied volatility can strongly predict stock market return volatility in developed economies. Hazman et al. (2018) emphasize that inadequate understanding of market dynamics and ineffective financial reforms can lead to systemic instability. Further a detailed review by Dhingra et al. (2024) indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. Therefore the investigation on stock market is important for scholars, investors and policymakers. The impact of economic fundamentals on stock returns or prices has been a topic of much discussion among experts and scholars. The Efficient Market Hypothesis, initially introduced by Fama in 1970 , asserts that in a market that operates efficiently, investors would not achieve exceptional profits as stock prices fully incorporate all pertinent information regarding fluctuations in macroeconomic variables. If the Efficient Market Hypothesis is assumed to be valid, any fluctuations in macroeconomic indicators should not exert a substantial influence on stock returns. Nevertheless, subsequent studies conducted by Fama and Schwert (1977) , Nelson (1976) , and other researchers have conducted a thorough assessment of the findings derived from the Efficient Market Hypothesis. These studies have verified that macroeconomic factors do indeed have an impact on stock prices, consequently influencing stock returns. A further theoretical contribution to the link between stock prices and the fundamentals of the economy is the Arbitrage Pricing Theory (APT) ( Ross, S.A. 1976 ; Chen et al., 1986 ). The link between macroeconomic variables and the stock market has been extensively studied in recent studies, building on previously established theoretical groundwork. The relationship between macroeconomic variables, stock market returns, and development in Ghana was assessed by Asravor, R. K., and Fonu, P. D. D. (2021) , offering insights that might be useful for developing nations like India. Luo, C., Qu, Y., Su, Y., and Dong, L. (2024) examine the transmission of risks from crude oil markets to China’s financial market, emphasizing the importance of crude oil prices, which are equally relevant for the Indian stock market. Theoretically, the study contribute to asset pricing models by indicating how change in macroeconomic indicators affects the stock price in Indian stock market. The study extends the applicability of models such as Arbitrage pricing theory in emerging markets, where the effect of macroeconomic indicators on stock price may differ significantly from developed markets. Previous research highlights the significance of GDP, inflation, crude oil prices, exchange rates, industrial production, and interest rates as key macroeconomic factors that impact the stock market. Fromentin, V. (2022) , Bildirici et al. (2022) , Ediriweera and Dissanayake (2022) , and Katmas and Indarningsih (2022) documented the importance of macroeconomic forces in international markets over time, whereas Yadav et al. (2022) , Shadab, A., and Sinha, A. K. (2022) , Rao, V., and Jyotreddy, K. (2022) , or Kumar et al. (2023) provide evidence from the Indian market. The hypothesis that portfolio holder expectations about the future values of macroeconomic variables can impact stock prices and that these variables become risk factors when stocks are substituted for other assets in the portfolio is supported by the studies listed above. So, it is very important to look into how much macroeconomic indicator affect the stock markets. Review of literature Many studies have looked at how the stock market is connected to macroeconomic elements. Previous studies indicate that macroeconomic variables and the stock market have an association. Fama (1981) sought to examine the link between stock returns, economic activity, inflation, and money in order to question preconceptions and offer new viewpoints on the dynamics of equities markets. While stock returns and real factors including output, capital expenditures, and the actual rate of return on capital show a positive link, the research results show a negative link between money growth rate and inflation. Olowe, R. A. (2007) employed Johansen’s vector correlation model to examine the dynamic linkage between macroeconomic variables (Industrial production, Inflation, interest rate, money supply, exchange rate and oil prices) and stock price in Nigerian Stock Exchange and found that stock price is positively affected by inflation, money supply, Oil price and interest rate while negatively by exchange rate and industrial production. Baranidharan, S., and Alex, A. (2020) found that effect of changes in exchange rate on Johannesburg Stock Exchange (JSE) was significant and negative. Neifar et al. (2021) studied the relationship between macroeconomic indicators (currency rate, consumer price index, and interest rate) and the stock market in the United Kingdom. The ARDL model shows that there is a long-term symmetric link between macroeconomic data and the price of stocks. Conversely, the ECM representation’s results indicate that the UK stock price is rapidly advancing toward long-term equilibrium as a result of all of the macroeconomic variables that were assessed. According to Pole, H., and Cavusoglu, B. (2021) , money supply and aggregate industrial output positively and significantly affect stock return volatility, whereas inflation and currency rates negatively affect it. This study used ARDL bonds to investigate the impact of macroeconomic variables on Nigerian stock market returns. Asravor, R. K., and Fonu, P. D. D. (2021) examined the connection between Ghana’s macroeconomic variables, stock market returns, and development revealed that interest rates and foreign direct investment were favourable to S.M. Development, but human capital, inflation, and money supply were awful. Using Jordan and Philips’ dynamic ARDL simulation model, Khan et al. (2023) studied exchange rate, gold, oil, and Shanghai stock exchange return effects. Although gold and oil prices positively affected return, the exchange rate negatively affected it. Chinese stock exchange rate fluctuations do not, in the long term, have a Granger-cause on stock returns, but they do have a substantial spillover effect, according to research by Cuestas and Tang (2021) . Okorie et al. (2021) investigated the correlation between Nigerian inflation, currency rates, and stock market returns by employing copulas. It has been determined that there is a minor positive correlation between Nigerian inflation and stock market returns, as well as a positive association between Nigerian inflation and the exchange rate of the Nigerian Naira against the US dollar. Hung (2022c) investigates the dynamic connectedness between macroeconomic data and the Chinese stock market through wavelet coherence analysis. Short-term macroeconomic variables affect the stock market, whereas long-term stock gains improve macroeconomic conditions. Javangwe and Takawira (2022) examined the South African stock market and currency rates using ARDL. The stock market is found to be adversely affected by exchange rate fluctuations in the long term. Using ECM, Katmas and Indarningsih (2022) investigated at how inflation, interest rates, and foreign exchange rates impacted the Indonesian Sharia Stock Index (ISSI) and found that BI interest has a long-term effect on the index, exchange rates have both short- and long-term effects, and inflation has no effect. Economic growth variables and the time valve of money were the two components identified by Ediriweera and Dissanayake (2022) using factor analysis. The Time Value of Money component substantially affects the Colombo Stock Exchange, but Economic Growth has little effect. Bildirici et al. established a complex link between VIX investor mood, gold prices, currency rates, oil prices, and stock market returns in 2022. Nevertheless, the Turkish lira and energy prices have a significant influence on stock market returns. Fromentin (2022) employed the time-varying Granger causality framework to identify asymmetric bidirectional causation, which implies that the lead-lag relationship between macroeconomic variables and US stock price is subject to change over time. Akanbi, A. (2025) found that GDP growth has a favourable, but negligible, impact on stock performance in Nigeria. Inflation, interest rate spread, and the macroeconomic environment as a whole, however, were found to have a negative but negligible impact on stock market performance. Thorbecke, W. (2025) found that throughout this time, the stock market was agitated by worries about inflation. The markets were further shaken by the Fed’s anti-inflationary measures. Rahmi et al. (2025) found that exchange rate has a significant effect on the green stock market, while effect of economic policy uncertainty, domestic interest rates, income, inflation, and oil prices is insignificant. Magnani et al. (2025) identified that macroeconomic variables are more predictable and stable and economic agents have more faith in the Brazilian stock market when the target set by the Brazilian Central Bank is credible. They further concluded that the stock market performs better when there is higher fiscal and monetary credibility. Gay (2008) concluded that no significant link exists between exchange rates, oil prices, and stock market index prices in any BRIC country, so implying a weak form of market efficiency in Brazil, Russia, India, and China by using the Box-Jenkins ARIMA model to investigate the correlation between macroeconomic variables and stock market index prices in BRIC nations. Verma, R. K., & Bansal, R. (2021) discovered that while gold prices have a negative impact on stock markets in both established and emerging economies, GDP, FDI (foreign direct investment), and FII (foreign institutional investment) had a positive impact. Salisu et al. (2021) investigated the potential of stock return differentials to anticipate exchange rate fluctuations in the BRICS countries, thereby evaluating the validity of the uncovered equity parity (UEP) theory for these countries. For three BRICS countries—Brazil, India, and South Africa—the research found a positive association between stock return differentials and exchange rate returns; results for China and Russia were opposite. Hung (2022b) using the MGARCH-DCC and Wavelet Coherence Transform approaches investigated the temporal and frequency link between worldwide crude oil, gold prices, and financial markets in the BRICS countries. The BRICS stock and exchange markets have long-term influence on crude oil and gold variables; yet, in the immediate term, the international commodities market greatly influences the BRICS financial markets. Hung (2022a) investigated the dynamic relationship and volatility spillover effects between exchange rates and stock returns in five Central and Eastern European (CEE) countries (Hung Hungary, Poland, the Czech Republic, Romania, and Croatia) and identified bidirectional volatility spillovers between the two financial markets in Hungary, the Czech Republic, and Croatia during the pre-crisis period; a unidirectional spillover of volatility from the stock market to the foreign exchange market was observed. Atif et al. (2022) evaluated the correlation between stock returns, exchange rates, and crude oil in emerging markets. Panel Granger causality revealed that the relationship between stock returns and oil prices has grown stronger since COVID-19 was designated a pandemic. The oil market and currency rates are two other factors that have a beneficial impact on stock performance. Hashmi, S. M., and Chang, B. H. (2023) discovered the positive relationship between E7 stock market and macroeconomic variables. Lone et al. (2023) found that selected macroeconomic variables affect the stock markets of all BRICS nations except Brazil in the long term. Singh, D. (2010) used granger causality test to study causal relation BSE sensex and macroeconomic variables. The result of Granger causality test depict bilateral causal relationship between BSE sensex and IIP, whereas unilateral causal relationship between BSE sensex and WIP. Pal, K., and Mittal, R. (2011) examined the relationship between Indian capital markets and macroeconomic indicators. The analysis found that inflation has a major impact on both the S&P CNX Nifty and the BSE Sensex, although only the former affects interest rates and the latter foreign currency rates. Kalra (2012) documented the Sensex and macroeconomic data link. The gold price, inflation rate, and foreign exchange rate have been regarded as the most important components in the development of Sensex forecast models. Singh (2014) found that while money supply and foreign investment favourably influence the Indian stock market, gold prices and currency values have a negative impact there. Gurloveleen and Bhatia (2015) discovered that the exchange rate and foreign institutional investors were significant factors after doing multiple regression analyses. Furthermore, there is no causal association between relevant factors and the average closing prices of BSE 500 manufacturing enterprises, according to the Granger causality test. Kotha and Bhawna (2016) found that money supply and inflation positively affect the Indian stock market return, whereas interest rates negatively affect it. Misra (2018) employed the Vector Error Correction Model (VECM) to analyse the BSE Sensex’s relationship with various macroeconomic variables, concluding that these variables, including money supply, inflation, interest rates, gold prices, foreign institutional investment, exchange rate, and the Index of Industrial Production, exhibited a long-term causal relationship with the Sensex. Mohanty et al. (2021) used the Auto Regressive Distributed Lag (ARDL) model to look into how macroeconomic factors affect stock market returns. While the unemployment rate and inflation have a negative effect, the Sensex is favourably influenced by the manufacturing to GDP ratio, debt to GDP ratio, US dollar, and GDP growth. Interest has a negative effect on share prices but a positive effect on consumer prices and industrial output, according to Syed (2021) , who looked at how macroeconomic events affect Bombay Stock Exchange share prices. Bhattacharjee, A., and Das, J. (2021) employed the autoregressive distributed lag (ARDL) bounds testing approach and the pair-wise Granger causality test to examine the long- and short-term relationships between domestic macroeconomic variables and the equities market. The exchange rate, wholesale price index, and money supply lag exert a considerable short-term effect, while the money supply and foreign currency exchange rate significantly influence the Indian stock market in the long run. Yadav et al. (2022) identified a substantial relationship between macroeconomic factors and the Indian stock market price using Johansen cointegration and sought long-run equilibrium using VECM. Using the GARCH model, Shadab, A., and Sinha, A. K. (2022) found a positive link between exchange rate and BSE Sensex. Rao, V., and Jyotreddy, K. (2022) investigated the relationship between stock price and macroeconomic variables using the ARDL model and found that, in the long run, the relationship is negligible; but, in the short run, foreign institutional investment and inflation positively influence stock prices while exchange rate has negative influence. Kumar et al. (2023) used the NARDL model to find that crude oil prices positively affect the Indian stock market and negatively affect the exchange rate, but gold prices had no effect. Keswani et al. (2024) found a positive long-term link of Indian stock market with GDP, disposable income, and FII engagement, while a negative correlation is found with inflation, interest rates, government policies, and currency rates. Hedau, A. (2024) Using logistic regression, researcher looked at macroeconomic factors influencing the performance of the NIFTY 50 index. They discovered that the Dow Jones index and changes in exchange rates are the primary drivers of the NIFTY 50 index. Experts, however, believe that predicting the movement of the NIFTY 50 index also requires consideration of other elements, such as political stability, the state of the developed, and India’s bilateral connections with other nations. Goel et al. (2025) found that Earnings per Share, Dividend Payout Ratio and Price-to-Book Ratio had a statistically significant relationship with stock returns. These variables indirectly get affected by change in macroeconomic variables due its effect on cost of production, demand of the products, financing pattern etc. Though past studies have examined the relationship, opinions and clarity on how macroeconomic events influence the market are lacking. Previous studies yield contradicting findings; some revealed positive associations while others revealed negative or none at all. Bhattacharjee, A., and Das, J. (2021) discovered no correlation. Although Tripathi, V., and Seth, R. (2014) discovered that the Wholesale Price Index had a favourable effect on the Indian stock market. While Syed, A. A. (2021) found negative influence of interest rate, further Bhattacharjee, A., and Das, J. (2021) reported no association. Furthermore, displaying varying results is the gold price; Bhattacharjee, A., and Das, J. (2020) found negative influence and later in 2021 found no correlation. Studies on exchange rates yield inconsistent results with Ratanapakorn, O., and Sharma, S. C. (2007) noting positive influence while Syed, A. A. (2021) recorded negative impact. Different conclusions highlight the complexity of the link between macroeconomic factors and stock market. It is necessary to offer unambiguous understanding on the link between macroeconomic variables and stock market. Few chosen variables have been used in past research at a time combined effect of all these variables is not investigated. To present unambiguous understanding on relationship between macroeconomic indicators and stock market and bridge this gap the study re-examins the relationship between macroeconomic indicators and indian stock market using ARDL approach combined with Granger causality test. Monthly data from April 2009 to March 2023 is used in the study. Methods In this paper we have used monthly data from April 2009 to March 2023 to study the impact of Wholesale price index (WPI), Consumer price index (CPI), Index of Industrial Production (IIP), Gross Domestic Product (GDP), Foreign Exchange Reserve (FER), Exchange Rate (ER), Silver Price (SP), Gold Prices (GP), Brent Crude Oil Prices (COP), Gross Fiscal Deficit (GFD), Value of Imports (IMP), Value of Exports (EXP), Foreign Institutional Investors (FII), Repo rate (REPO), Interest Rate (Long Term 10 yr) (INT), Call Money Rate (CMR), Balance of Payment (BOP), Broad Money (M3) on National Stock Exchange returns (RNSE) ( Table 1 ). This time period captures a broad range of macroeconomic conditions and market cycles, that includes post – global financial crisis recovery, major policy reforms (such as GST, demonetisation) and impact of the COVID – 19 pandemic. This data period provides more comprehensive investigation of how macroeconomic indicators influence stock price varying economic conditions. All the variables are converted into its logarithmic values for analysis. The autoregressive distributed lag (ARDL) bounds testing approach is used to investigate long-term correlations between variables. Pesaran et al . created it in 2001 and for Causality relationship Granger causality test is employed. Time series that are neither stationary nor have a fixed order of integration can be fitted using the ARDL model, which is based on ordinary least squares (OLS). Traditional econometric techniques Ordinary Least Squares regression (OLS) or vector autoregressive (VAR) model can be applied only when all variables are stationary I(0) and Johansen cointegration test can be applied in case all the variables are non stationary I(1). However ARDL model is abe to handle variables integrated at mixed orders (I(0) or I(1)) and provides strong findings even in a small sample size. This model can be used to study short term as well long term relationship. ARDL model is also used in Studies conducted by Bhattacharjee, A., and Das, J. (2021) , Neifar et al. (2021) , Syed et al. (2021) , and Javangwe and Takawira (2022) . Figure 1. Methodological Process (Author’s compilation). Table 1. Variables of the study. S.No Variables Symbol Data Source Unit of measurement 1 Nifty50 NSE Investing.com Return 2 Wholesale price index WPI Investing.com Annual growth rate (%) 3 Consumer price index CPI Investing.com Annual growth rate (%) 4 Index of Industrial Production IIP Investing.com % change previous period 5 Gross Domestic Product GDP Investing.com % change previous period 6 Foreign Exchange Reserve FER RBI INR (Cr) 7 Exchange Rate ER Investing.com INR/USD 8 Silver Price SP Yahoo Finance USD 9 Gold Prices GP Investing.com INR 10 Brent Crude Oil Prices COP U.S. Energy Information Administration (EIA) USD/Barrel 11 Gross Fiscal Deficit GFD RBI INR (Cr) 12 Value of Imports IMP IMF USD (Millions) 13 Value of Exports EXP IMF USD (Millions) 14 Foreign Institutional Investors FII Central Depository Services (India) Limited INR (Cr) 15 Repo rate REPO bankbazaar.com Percentage 16 Interest Rate (Long Term 10 yr) INT OECD % per annum 17 Call Money Rate CMR RBI % P.a. 18 Balance of Payment BOP RBI INR (cr) 19 Money Supply M2 Federal Reserve Economic Data (FRED) INR The current study applied Pesaran et al. (2001) Autoregressive Distributed Lag (ARDL) model. This method is applied since it provides strong findings when the dataset is small and the variables show a mix of integration orders, that is, I(0) and I(1) variables. We must first make sure none of the variables are stationary at second difference before using the ARDL model. We investigate at which level variables are stationary using the Augmented Dickey-Fuller ( Phillips & Perron, 1988 ). The null hypothesis posits the existence of a unit root, while the alternative hypothesis suggests stationarity. The augmented Dickey-Fuller test conducts the regression analysis described below. (1) y t = a + β t + γ y t − 1 + ∑ i = 1 p δ i ∆ y t − i + e t y ( t -1) = lag 1 of time series and delta y ( t -1) = first difference of the series at time ( t -1) Fundamentally, it has a similar null hypothesis as the unit root test. That is, the coefficient of y ( t -1) is 1, implying the presence of a unit root. If not rejected, the series is taken to be non-stationary. ADF test results assist ARDL application to investigate co-integration among the variables. If the variables are stationery at level Johansen cointegration test is appropriate to investigate the long-term relationship among the variables. The equation used in the present study to specify the ARDL model is as follows: (2) ∆ y t = α 0 + ∑ i = 1 p α 1 ∆ y t − i + ∑ i = 0 q α 2 ∆ x t − i + δ 1 y t − 1 + δ 2 x t − 1 + ε t Where x t vector of regressors, y t dependent variable; α 0 intercept; Δ is the first difference operator; α i represent short-run coefficient; δ i represent long-run coefficient; p and q represent restricted lags and ε t shows error term. The estimated F-statistic indicates that the variables are cointegrated, therefore assuming a long-term link between them, if it surpasses the upper bound critical value. The test is said to be inconclusive, on the other hand, if the F-statistic goes below the upper bound critical value but stays above the lower bound critical value. It follows from an F-statistic smaller than the lower bound critical value that the variables are not cointegrated. Should the ARDL limits test verify cointegration, the long-run coefficients and short-run parameters including the error correction term are next to be estimated ( Shrestha, M. B. & Bhatta, G. R., 2018 ). We then evaluate the model’s dependability with several diagnostic tests. The functional form of the model is checked using Ramsey’s RESET test; heteroscedasticity is tested using Breusch-Pagan–Godfrey test; serial correlation between the residuals is tested using Breusch-Godfrey serial correlation lag range multiplier (LM); and CUSUM test checks the stability of the coefficients. We investigated the direction of short-term causality using the pairwise Granger causality test ( Granger, C. W., 1969 ). Analysis and Discussion Time series analysis, ascertaining the stationarity status is a crucial step. This study employed the Augmented Dickey-Fuller test to ascertain the stationarity status ( Table 2 ). The ADF test results demonstrate that LnIIP, LnGDP, and LnFII are stationary at level, but the remaining variables in the study are stationary at first difference. At the level, the null hypothesis that the series possesses a unit root is rejected for LnIIP, LnGDP, and LnFII, while it cannot be rejected for the remaining variables in the study. Upon the initial differencing, all non-stationary series attain stationarity. Table 2. Unit Root Test (ADF). Null hypothesis: Series has a unit root Level First difference Order of integration t-Statistic p-Value t-Statistic p-Value LnNSE 0.65331 0.8540 10.66795 0.0000 I(1) LnWPI 2.86468 0.0518 4.34557 0.0005 I(1) LnSP 1.85264 0.3541 6.39621 0.0000 I(1) LnREPO 0.12702 0.9669 4.35494 0.0005 I(1) LnM3 2.66303 0.0827 2.93487 0.0436 I(1) LnINT 1.59587 0.4825 4.88953 0.0001 I(1) LnIMP 2.92610 0.0445 8.53574 0.0000 I(1) LnIIP 6.05275 0.0000 I(0) LnGP 2.25023 0.1896 6.64226 0.0000 I(1) LnGFD 2.73658 0.2541 6.39573 0.0000 I(1) LnGDP 4.03958 0.0016 I(0) LnFII 9.05679 0.0000 I(0) LnFER 1.50637 0.5281 3.54933 0.0079 I(1) LnEXP 2.78522 0.0625 7.85539 0.0000 I(1) LnER 1.64973 0.4550 5.14721 0.0000 I(1) LnCPI 2.02833 0.2746 9.32421 0.0000 I(1) LnCOP 1.58457 0.4882 6.58330 0.0000 I(1) LnCMR 1.55676 0.5024 6.17225 0.0000 I(1) LnBOP 4.32833 0.0006 I(0) Subsequent to establishing the stationarity of the variables, we employed the ARDL bounds test to investigate the presence of a long-run relationship among the variables. Table 3 presents the findings of the Autoregressive Distributed Lag Bounds Test for cointegration, indicating a significant long-term equilibrium link among the variables. The F-statistic of 7.117339, obtained from the test, exceeds the upper bound critical values at all conventional significance levels: 2.77 at 10%, 3.04 at 5%, 3.28 at 2.50%, and 3.61 at 1%. The null hypothesis of no cointegration is rejected, confirming a long-term link between the examined variables. Table 3. ARDL Bounds Test. Test statistic Value Significance level I(0) I(1) F-statistic 7.117339 10% 1.76 2.77 5% 1.98 3.04 2.50% 2.18 3.28 1% 2.41 3.61 The long-run ARDL results shown in Table 4 show that money supply (M3), GDP, foreign institutional investment (FII), exports (EXP), exchange rates (ER), and crude oil prices (COP) has statistically significant effect on the stock price. LnM3, LnGDP, LnFII, andLnCOP have positive coefficients indicating that stock price rises follow from changes in these variables. Should M3, GDP, FII, and COP rise by 1% stock price, the respective increases will be 1.37%, 0.12%, 0.09% and 0.30%. By contrast, LnEXP and LnER have negative coefficients, meaning a negative effect on stock price. Regarding the money supply, the outcomes contradict Asravor, R. K., & Fonu, P. D. D. (2021) and are consistent with Olowe, R. A. (2007) , and Pole, H., and Cavusoglu, B. (2021) . For GDP results match Parab, N., and Reddy, Y. V. (2020) and Abu-Libdeh, H., and Harasheh, M. (2011) . For exchange rate results are consistant with Khan et al. (2023) , Baranidharan, S., & Alex, A. (2020) and Olowe, R. A. (2007) . Findings regarding FII are consistant with Verma, R. K., & Bansal, R. (2021) . Further crude oil price results contradict ( Abuoliem et al. , 2019 ) but support Jain, A., and Biswal, P. C. (2016) . Table 4. Long Run Form of ARDL. Model (2, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0) Variable Coefficient Std. Error t-Statistic p-Value LnWPI 0.022254 0.037985 0.585859 0.5589 LnSP -0.306743 0.180919 -1.69547 0.0922*** LnREPO -0.718907 0.377801 -1.902871 0.0591*** LnM3 1.37184 0.470784 2.913945 0.0042* LnINT 0.499693 0.350984 1.423692 0.1568 LnIMP -0.030099 0.203991 -0.147549 0.8829 LnIIP 0.06365 0.052582 1.21051 0.2282 LnGP 0.329263 0.333996 0.985829 0.3259 LnGFD -0.002413 0.011244 -0.214605 0.8304 LnGDP 0.127941 0.05071 2.522993 0.0128** LnFII 0.099797 0.024157 4.1312 0.0001* LnFER 0.031715 0.454223 0.069822 0.9444 LnEXP -0.590043 0.295944 -1.993767 0.0481** LnER -1.24941 0.552302 -2.262188 0.0252** LnCPI -0.109402 0.056085 -1.950641 0.0531*** LnCOP 0.302682 0.119182 2.539658 0.0122** LnCMR 0.131846 0.194867 0.676594 0.4998 LnBOP -0.011307 0.008135 -1.389949 0.1668 C -12.52415 3.264108 -3.836927 0.0002 With coefficients of -0.306743, -0.7189 and -0.1094, respectively, the Silver Price (SP), repo rate (REPO) and consumer price index (CPI) show negative associations, significant at the 10% level. Other variables, such as the wholesale price index, long-term interest rates, imports, industrial production index, gold price, gross fiscal deficit, foreign currency reserves, call money rate, and balance of payments, do not have statistically significant long-term effects. The model’s constant term is significant, as indicated by its coefficient of -12.5241. These findings emphasize the significant influences on the future behaviour of the dependent variable, which include money supply, GDP, foreign institutional investment, exports, currency rates, and crude oil prices. The error correction model results ( Table 5 ) reveal the short-term dynamics and the pace of adjustment towards the long-run equilibrium. The highly significant and negative error correction coefficient implies that approximately 22.74% of the deviation from the long-run equilibrium is rectified within one month. Lag of stock price, value of import, and exchange rate has significant negative impact on stock price. Impact of foreign exchange reserve is positive and significant. Furthermore, the model demonstrates strong explanatory power, with an R-squared value of 0.677981, highlighting its robustness in capturing the interrelationships among the variables. Table 5. Error Correction form. Variable Coefficient Std. Error t-Statistic p-Value D (LnNSE(-1)) -0.132218 0.048813 -2.70869 0.0076** D (LnSP) -0.000665 0.031748 -0.020939 0.9833 D (LnIMP) -0.105707 0.036631 -2.885751 0.0045** D (LnIIP) 0.000404 0.006163 0.065519 0.9479 D (LnGP) -0.059503 0.058602 -1.015377 0.3117 D (LnFER) 0.385728 0.129725 2.973437 0.0035** D (LnER) -1.37868 0.13982 -9.860403 0.0000* D (LnCPI) 0.020965 0.015267 1.373214 0.1719 CointEq(-1)* -0.227427 0.017871 -12.72577 0.0000* R-squared 0.677981 To check reliability of the short run estimates Ramsey RESET and Breusch–Pagan–Godfrey Heteroskedasticity Test to check the reliability the short run estimates and CUSUM plot to test stability of the model. The Ramsey RESET test yields an F-statistic of 0.075087 with a p-value of 0.7845, indicating no evidence of model misspecification. The Breusch-Pagan-Godfrey test for heteroskedasticity provided an F-statistic of 0.953374 with a p-value of 0.5367, suggesting the residuals exhibit homoscedasticity ( Table 6 ). CUSUM plot presented in Figure 2 indicate that the model is stable and outcomes are reliable. Table 6. Residual diagnostic test. Test statistics p-Value Ramsey RESET Test, F -statistic 0.075087 0.7845 Heteroskedasticity Test: Breusch-Pagan-Godfrey, F -statistic 0.953374 0.5367 Figure 2. CUSUM plot. Granger causality examined the significance of the coefficients of lagged y t , which are used as the explanatory variables for x t in the regression context. For a simple bivariate model, one can test the following equation: (3) x t = α 0 + ∑ i = 1 p α i y t − i + ∑ j − 1 q β j x t − j + u t (4) y t = α 0 + ∑ i = 1 p β i x t − i + ∑ j − 1 q α j y t − j + ε t Where Ho: y does not Granger causes x in the first regression equation and x does not Granger causes y in the second regression equation. Results of pairwise Granger causality test ( Table 7 ) Indicate that there is bidirectional causality between IMP and NSE, FII and NSE, FER and NSE, COP and NSE signifying the presence of feedback mechanism between the variables. Unidirectional causality is identified between WIP and NSE, SP and NSE, GDP and NSE, EXP and NSE, ER and NSE. Result indicates No causality between REPO and NSE, M3 and NSE, INT and NSE, IIP and NSE, GP and NSE, GFD and NSE, CPI and NSE, CMR and NSE, BOP and NSE. Pairwise Granger Causality Test findings suggest that IMP, FII, FER, COP, WIP, SP, GDP, EXP and ER are helpful in stock price prediction in Indian stock market. Table 7. Pairwise Granger Causality Tests. Pair Null Hypothesis: F-Statistic p-Value Result WIP and NSE LNWPI does not Granger Cause LNNSE 0.59491 0.6192 Unidirectional causality LNNSE does not Granger Cause LNWPI 2.72148 0.0463** SP and NSE LNSP does not Granger Cause LNNSE 2.05970 0.1078 Unidirectional causality LNNSE does not Granger Cause LNSP 3.03723 0.0308** REPO and NSE LNREPO does not Granger Cause LNNSE 1.74601 0.1598 No causality LNNSE does not Granger Cause LNREPO 0.76984 0.5125 M3 and NSE LNM3 does not Granger Cause LNNSE 1.68342 0.1728 No causality LNNSE does not Granger Cause LNM3 0.50557 0.6790 INT and NSE LNINT does not Granger Cause LNNSE 1.15080 0.3305 No causality LNNSE does not Granger Cause LNINT 1.45463 0.2291 IMP and NSE LNIMP does not Granger Cause LNNSE 2.61472 0.0531*** Bidirectional causality LNNSE does not Granger Cause LNIMP 14.41820 0.0000* IIP and NSE LNIIP does not Granger Cause LNNSE 0.59025 0.6223 No causality LNNSE does not Granger Cause LNIIP 0.57354 0.6332 GP and NSE LNGP does not Granger Cause LNNSE 0.93629 0.4247 No causality LNNSE does not Granger Cause LNGP 0.75005 0.5239 GFD and NSE LNGFD does not Granger Cause LNNSE 0.01070 0.9985 No causality LNNSE does not Granger Cause LNGFD 0.27888 0.8406 GDP and NSE LNGDP does not Granger Cause LNNSE 1.76157 0.1567 Unidirectional causality LNNSE does not Granger Cause LNGDP 8.30001 0.0000* FII and NSE LNFII does not Granger Cause LNNSE 2.50823 0.0609*** Bidirectional causality LNNSE does not Granger Cause LNFII 2.99270 0.0327** FER and NSE LNFER does not Granger Cause LNNSE 4.47503 0.0048* Bidirectional causality LNNSE does not Granger Cause LNFER 2.29608 0.0798*** EXP and NSE LNEXP does not Granger Cause LNNSE 0.75034 0.5237 Unidirectional causality LNNSE does not Granger Cause LNEXP 10.99250 0.0000* ER and NSE LNER does not Granger Cause LNNSE 2.13205 0.0983*** Unidirectional causality LNNSE does not Granger Cause LNER 1.99846 0.1164 CPI and NSE LNCPI does not Granger Cause LNNSE 0.87646 0.4547 No causality LNNSE does not Granger Cause LNCPI 0.56382 0.6397 COP and NSE LNCOP does not Granger Cause LNNSE 2.18345 0.0921*** Bidirectional causality LNNSE does not Granger Cause LNCOP 7.28678 0.0001* CMR and NSE LNCMR does not Granger Cause LNNSE 1.14049 0.3346 No causality LNNSE does not Granger Cause LNCMR 0.74170 0.5287 BOP and NSE LNBOP does not Granger Cause LNNSE 0.09832 0.9609 No causality LNNSE does not Granger Cause LNBOP 1.23910 0.2974 Impulse reponse function Response of stock market to a unit shock in macroeconomic variables is presented in Figure 3 . It can be observed that NSE initially increases and start decreasing after some periods in response to one standard deviation shock created in REPO, IMP, IIP, ER and CPI. It initially Decreases and start increasing after some periods in response to one standard deviation shock created in GFD, GDP, FII, EXP, COP, BOP and WPI. NSE increases in response to one standard deviation shock created in M3, INT and FER, while decreases in response to GP, CMR and SP. Figure 3. Impulse response function result. Conclusion and Recommendations The ARDL bonds test validates the presence of an enduring connection between the variables. The ARDL model demonstrates that over a long period of time, the money supply (M3), gross domestic product (GDP), foreign institutional investment (FII), and crude oil prices (COP) have a substantial positive influence on the stock price. Conversely, exchange rates (ER) and exports (EXP) have a major negative impact. In the near term, there is a large negative correlation between the lag of stock price, value of imports, and exchange rate with stock price. However, the influence of foreign exchange reserves on stock price is positive. The diagnostic tests performed on the residuals to confirm the accuracy and consistency of the short-run parameters indicated no signs of serial correlation, heteroscedasticity, or misspecification. The findings of the paired Granger causality test indicate that the domestic macroeconomic variables have predictive value for stock prices in the Indian stock market. Therefore, it is important to closely watch these variables. It is important to note that granger causality test is a useful statistical tool however; it does not show true causality. It indicated that one variable contains information useful for predicting another variable, but this may be due to some underlying factor. Policy Recommendations The study’s findings suggest that macroeconomic variables have an impact on the Indian stock market both in the short term and the long term. Hence, it is imperative for investors and portfolio managers to regularly monitor the prevailing macroeconomic conditions in order to optimize their returns and mitigate potential risks. Furthermore, the finding has significant policy consequences. A rise in the money supply has a beneficial impact on the Indian stock market by enhancing a firm’s capacity to produce and borrow. The Reserve Bank of India (RBI) should strive to maintain an optimal level of money supply by utilizing open market operations and monetary policy. This is crucial because an excessive amount of money in circulation can lead to inflation, which has a detrimental impact on the Indian stock market. Targeted Long-Term Repo Operations (TLTRO), Open market operations, and policy rate adjustments are some measures that RBI could adopt to balance money supply without triggering inflation. Furthermore, the exchange rate has a detrimental effect on the Indian stock market. The Reserve Bank of India (RBI) should implement measures to mitigate fluctuations in the exchange rate. During Taper tantrum of 2013, RBI sucessfully stabilized exchange rate through policy interventions. The Indian government should formulate policies to bolster the export sector and facilitate the import of diverse items. The Atmanirbhar Bharat Abhiyan and Make in India initiatives are remarkable endeavours in this context; yet, the effectiveness of these initiatives relies on meticulous execution. Incentive for export oriented sectors, production linked incentives, Improve trade infrastructure, Enhance free trade agreements, Promote R&D, Trade facilitation for small exporters, expanding access to export credit facilities are some steps that can strengthen Indian export eco system and align with broader objectives of ‘Make in India’ and ‘Atmanirbhar Bharat’. The positive impact of GDP underscores the significance of continuous economic expansion, necessitating policies that focus on improving productivity, developing infrastructure, and creating a favourable business environment. Enhancing the inflow of Foreign Direct Investment (FDI) and Foreign Institutional Investment (FII) can significantly enhance economic performance. This necessitates the establishment of a stable and conducive regulatory framework that is attractive to investors. The study has some limitations. First, The does not consider variables like Invertor sentiment, Global economic events, unemployment rate and economic policy uncertainty that may affect return in Indian stock markets. Secondly, Their may be existence of reverse causality in case of some macroeconomic variables. Further, Structural changes in Indian economy such as Demonetization, GST implementation and COVID-19 may have affected Indian stock market during the study period. These structural changes can not be captured using ARDL model, future studies may use structural break test to study effect of these structural changes. Ethics and consent Ethical approval and consent were not required. Data availability statement The underlying data related to the paper are available in figshare with the following citation and DOI: https://doi.org/10.6084/m9.figshare.27044473 ( Sanjay Singh Chauhan et al. , 2024 ) A causality investigation into stock prices and macroeconomic indicators in the Indian stock market © 2024 by Sanjay Singh Chauhan, Dr. Pradeep Suri, Dr. Debapriyo Nag, Dr. Farman Ali is licensed under CC BY 4.0 Attribution 4.0 International. References Abu-Libdeh H, Harasheh M: Testing for correl--ation and causality relationships between stock prices and macroeconomic variables the case of Palestine Securities Exchange. Int. Rev. Bus. Res. Pap. 2011; 7 (5): 141–154. Abuoliem N, Nor SM, Matar A, et al. : Crude oil prices, macroeconomic indicators, and the financial sector in Jordan: Dynamic causes and responses. J. Int. Stud. 2019; 12 (3): 131–146. 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Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 30 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Management, Uttaranchal University, Dehradun, Uttarakhand, 248007, India 2 College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh, 11673, Saudi Arabia Sanjay Singh Chauhan Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software Pradeep Suri Roles: Supervision, Validation Firoz Alam Roles: Investigation, Writing – Original Draft Preparation Umme Hani Roles: Conceptualization, Formal Analysis Amar Johri Roles: Methodology, Visualization, Writing – Original Draft Preparation Farman Ali Roles: Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 03 Jun 2025, 13:1299 https://doi.org/10.12688/f1000research.157041.3 version 2 Revised Published: 02 May 2025, 13:1299 https://doi.org/10.12688/f1000research.157041.2 version 1 Published: 30 Oct 2024, 13:1299 https://doi.org/10.12688/f1000research.157041.1 Copyright © 2025 Singh Chauhan S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Singh Chauhan S, Suri P, Alam F et al. A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.12688/f1000research.157041.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 03 Jun 2025 Revised Views 0 Cite How to cite this report: Samsudin H. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.183120.r389494 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v3#referee-response-389494 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Jun 2025 Hazman Samsudin , The Universiti Malaysia Terengganu, Terengganu, Malaysia Approved VIEWS 0 https://doi.org/10.5256/f1000research.183120.r389494 I now recommend the ... Continue reading READ ALL I now recommend the manuscript to be indexed. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Finance and International economics, econometrics, development economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Samsudin H. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.183120.r389494 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v3#referee-response-389494 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Aijaz Syed A. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.183120.r389495 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v3#referee-response-389495 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Jun 2025 Aamir Aijaz Syed , National Institute of Technology, Allahabad, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.183120.r389495 I am satisfied with the revised ... Continue reading READ ALL I am satisfied with the revised version and approve the version shared. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Finance and Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Aijaz Syed A. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.183120.r389495 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v3#referee-response-389495 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 02 May 2025 Revised Views 0 Cite How to cite this report: Aijaz Syed A. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.179731.r382758 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v2#referee-response-382758 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 15 May 2025 Aamir Aijaz Syed , National Institute of Technology, Allahabad, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.179731.r382758 The author should include a clear theoretical contribution in the introduction section. Literature review is adequate however in my opinion author is suggested to include comprehensive literature gap and how the present study fill this gap. ... Continue reading READ ALL The author should include a clear theoretical contribution in the introduction section. Literature review is adequate however in my opinion author is suggested to include comprehensive literature gap and how the present study fill this gap. The author should highlight the reason for employing the data from 2009 to 2023. Author can add few recent studies: Company-specific antecedents to stock returns: a statistical investigation with special reference of the NIFTY 50. (refer 1 ) Methodology is appropriate however I recommend the author should add few study strengthening the rational for employing ARDL method. Author can refer the following studies: Investigating the impact of the first and second waves of the COVID-19 pandemic on the Indian stock and commodity markets: An ARDL analysis of gold, oil, and stock market prices. (refer 2 ) . The author can strengthen the outcome using empirical literature which is missing Finally, policy specific recommendations need to be added. Best Wishes Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Goel, S., Yadav, N., Wani, N. U. H., & Syed, A. A. (2025). Company-specific antecedents to stock returns: a statistical investigation with special reference of the NIFTY 50. International Journal of Diplomacy and Economy, 11(2), 105-123. 2. Syed, A., Tripathi, R., & Dewan, J. (2021). Investigating the Impact of COVID-19 Pandemic First and Second Wave on the Indian Stock and Commodity Market: An ARDL Analysis of Gold, Oil and Stock Market Price. Syed A. A, Tripathi. R, Dewan Jyoti. Investigating the impact of COVID, 19, 8-21. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Finance and Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Aijaz Syed A. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.179731.r382758 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v2#referee-response-382758 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 11 Aug 2025 Sanjay Chauhan , $usrAffiliation 11 Aug 2025 Author Response The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. The above suggestions have been incorporated into version 3 of the article. Thank You The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 11 Aug 2025 Sanjay Chauhan , $usrAffiliation 11 Aug 2025 Author Response The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. The above suggestions have been incorporated into version 3 of the article. Thank You The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 30 Oct 2024 Views 0 Cite How to cite this report: Samsudin H. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r369212 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-369212 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Mar 2025 Hazman Samsudin , The Universiti Malaysia Terengganu, Terengganu, Malaysia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.172441.r369212 1. In the introduction, the author should address why an investigation on stock market is important. Perhaps the author should add some evidence on stock market cause a volatility. this have been documented such as in Hazman (2016)(refer 4), ... Continue reading READ ALL 1. In the introduction, the author should address why an investigation on stock market is important. Perhaps the author should add some evidence on stock market cause a volatility. this have been documented such as in Hazman (2016)(refer 4), Dai et al., (2020)(refer 3) and Dhingra et al., (2024)(refer 2). Please cite these articles and highlights why it is important to study on stock market. 2. Literature review is fine but can be improve by writing it in the manner of discussions rather than just a list of literature. 3. The method is a bit confusing. The title said a causality investigation and in result the author did mention about Granger causality, but the method used is ARDL. Please note that ARDL is cointegration test and not causality testing. Causality test is more suitable with Granger test and in the case of mixed stationarity as suggested by this study, Toda-Yamamoto causality testing seems more feasible and suitable. So this study investigating causality relationship or cointegration among the variables? please clarify and make it clear. and also are the data is in original form or have been log? 4. The discussion on the results also a bit shallow. please elaborate more. I think it would be beneficial to cite this recent article in 2025 also- refer 1 Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Rahmi N, Samsudin H, Ahmat N: The Dynamic Response of the Green Stock Market to External Economic Policy Uncertainty: The Case of Indonesia. International Journal of Energy Economics and Policy . 2025; 15 (2): 360-369 Publisher Full Text 2. Dhingra B, Batra S, Aggarwal V, Yadav M, et al.: Stock market volatility: a systematic review. Journal of Modelling in Management . 2024; 19 (3): 925-952 Publisher Full Text 3. Dai Z, Zhou H, Wen F, He S: Efficient predictability of stock return volatility: The role of stock market implied volatility. The North American Journal of Economics and Finance . 2020; 52 . Publisher Full Text 4. Samsudin, Hazman & Nawawi, Mohd Nasir & Abdul Halim, Zairihan & Md Said, Ahmad Syahmi, 2018. "Financial Performance Evaluation of Islamic Banking System: A Comparative Study among Malaysia’s Banks," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 137-147. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Finance and International economics, econometrics, development economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Samsudin H. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r369212 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-369212 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 09 Aug 2025 Sanjay Chauhan , $usrAffiliation 09 Aug 2025 Author Response The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. The above suggestions have been incorporated into version 3 of the article. Thank You The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 09 Aug 2025 Sanjay Chauhan , $usrAffiliation 09 Aug 2025 Author Response The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. The above suggestions have been incorporated into version 3 of the article. Thank You The above suggestions have been incorporated into version 3 of the article. Thank You Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Askarzadeh A. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r368214 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-368214 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Mar 2025 Alireza Askarzadeh , Old Dominion University, Norfolk, Virginia, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.172441.r368214 Summary of the Paper The paper investigates the causal relationship between macroeconomic indicators and stock prices in the Indian stock market using the ARDL bounds testing approach and Granger causality tests. The study analyzes data from April 2009 to ... Continue reading READ ALL Summary of the Paper The paper investigates the causal relationship between macroeconomic indicators and stock prices in the Indian stock market using the ARDL bounds testing approach and Granger causality tests. The study analyzes data from April 2009 to March 2023, examining the long-run and short-run effects of variables such as money supply, foreign institutional investment (FII), crude oil prices, exchange rates, and GDP on stock market returns. The findings highlight significant predictive relationships, with policy implications for investors and regulators. While the paper presents a well-structured empirical analysis, there are several areas for improvement, particularly regarding methodological justification, causality interpretation, and the issue of endogeneity. Major Concerns 1. Endogeneity Issues (Reverse Causality & Omitted Variable Bias) The study does not address potential endogeneity concerns, which are critical in empirical finance research. The relationship between macroeconomic indicators and stock prices is likely bidirectional : Reverse Causality: Stock prices themselves influence macroeconomic variables such as exchange rates and FII inflows, making it difficult to establish one-way causation. Omitted Variable Bias: External factors such as global market trends, investor sentiment, or monetary policy shocks could simultaneously affect both stock prices and macroeconomic indicators. Recommendation: The authors should acknowledge this limitation and consider addressing it through: Instrumental Variable (IV) methods (e.g., lagged macroeconomic indicators or global oil price shocks). Structural Vector Autoregression (SVAR) models to capture dynamic interactions. Event study approaches if identifiable policy shocks exist. Without addressing these concerns, the causal claims remain associational rather than truly causal . 2. Justification of Model Choice The use of the ARDL model is reasonable, but the authors should provide a stronger justification for selecting it over other alternatives like VAR or VECM. Specifically: 3. Interpretation of Causality The Granger causality test does not establish true causation but only predictive relationships. This should be made explicit in the discussion. 4. Expanding Literature Comparison The paper would benefit from a stronger comparison with similar studies in emerging markets. Studies from China, Brazil, and South Africa have examined similar relationships and should be referenced. This would help position the paper’s findings within a broader research context. 5. Strengthening Policy Implications The policy recommendations are insightful but remain broad. The authors should: Minor Concerns Presentation of Results : Including impulse response functions or time-series graphs would improve the clarity of findings. Significance Levels in Tables : The authors should explicitly report significance levels (p-values) for each coefficient in the ARDL model. Limitations Section : A dedicated discussion of omitted variables, structural economic shifts (e.g., COVID-19), and potential model improvements should be included. Conclusion This study makes an important contribution to understanding the interaction between macroeconomic indicators and stock prices in India. However, several methodological concerns—particularly regarding endogeneity, causality interpretation, and robustness—should be addressed to strengthen the paper’s scientific rigor. Recommendation: Revision By incorporating these suggestions, the paper will provide a more compelling and reliable contribution to the literature on stock market-macroeconomic interactions. Sincerely, Alireza Askarzadeh Finance Department Old Dominion University Norfolk, VA, USA Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Corporate finance, Investments I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Askarzadeh A. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r368214 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-368214 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 02 May 2025 Sanjay Chauhan , $usrAffiliation 02 May 2025 Author Response Respected Sir, I have incorporated most of your suggestions in the revised version. New version includes Impulse response function, limitations and P- Value in ARDL model. Further, I have also ... Continue reading Respected Sir, I have incorporated most of your suggestions in the revised version. New version includes Impulse response function, limitations and P- Value in ARDL model. Further, I have also expanded the literature review for comparison as suggested. Thanks and Regards Sanjay Singh Chauhan Respected Sir, I have incorporated most of your suggestions in the revised version. New version includes Impulse response function, limitations and P- Value in ARDL model. Further, I have also expanded the literature review for comparison as suggested. Thanks and Regards Sanjay Singh Chauhan Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 02 May 2025 Sanjay Chauhan , $usrAffiliation 02 May 2025 Author Response Respected Sir, I have incorporated most of your suggestions in the revised version. New version includes Impulse response function, limitations and P- Value in ARDL model. Further, I have also ... Continue reading Respected Sir, I have incorporated most of your suggestions in the revised version. New version includes Impulse response function, limitations and P- Value in ARDL model. Further, I have also expanded the literature review for comparison as suggested. Thanks and Regards Sanjay Singh Chauhan Respected Sir, I have incorporated most of your suggestions in the revised version. New version includes Impulse response function, limitations and P- Value in ARDL model. Further, I have also expanded the literature review for comparison as suggested. Thanks and Regards Sanjay Singh Chauhan Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Subburayan B. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r337233 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-337233 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Nov 2024 Baranidharan Subburayan , Christ University, Bangalore, Karnataka, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.172441.r337233 Your analysis provides a strong foundation, examining both short-term and long-term impacts of macroeconomic variables on the Indian stock market. The findings hold significance for investors and policymakers, and you present a clear framework for policy recommendations. However, to further ... Continue reading READ ALL Your analysis provides a strong foundation, examining both short-term and long-term impacts of macroeconomic variables on the Indian stock market. The findings hold significance for investors and policymakers, and you present a clear framework for policy recommendations. However, to further enhance the rigour, clarity, and scientific soundness of your study, consider the following constructive suggestions 1. Clarify the Methodological Approach While using the ARDL model is appropriate, it would be beneficial to justify why this model was selected over other econometric techniques, especially for readers less familiar with time-series methodologies. Could you briefly discuss why ARDL is suitable for analyzing the relationships between non-stationary variables? The diagnostic tests ensure the robustness of the model, yet further clarification on which specific tests (e.g., Breusch-Godfrey for serial correlation or Breusch-Pagan for heteroscedasticity) were used would enhance transparency. Detail the results and thresholds applied to interpret the presence or absence of statistical issues. 2. Strengthen the Discussion on Causality and Interpretation While the Granger causality test is a useful tool for examining predictiveness, it does not confirm causation. A brief statement on this limitation would enhance scientific accuracy, as it would clarify that observed relationships may not imply a causal impact. The findings suggest differing influences of variables like FII and COP in the short vs. long term. A deeper exploration of why these factors have opposing impacts over different time horizons could improve interpretative depth. 3. Expand the Literature Review and Comparison Comparing your findings to those of similar studies in emerging markets or other Asian economies can underscore the unique or common effects of macroeconomic variables in the Indian context. Specifically, mention studies on how money supply, exchange rates, and FII impact stock prices in comparable economies, discussing any consistencies or contradictions. Many of the policy recommendations align with existing government initiatives. Strengthen this section by drawing connections with specific studies or historical examples where similar macroeconomic interventions (e.g., exchange rate management or increased FII) led to observable outcomes. 4. Improve the Presentation of Results Visual representations (e.g., impulse response functions or time-series graphs of each variable’s impact over time) could improve the clarity of your results, especially for capturing short-run vs. long-run effects. Ensure that the significance levels (p-values) of each coefficient in the ARDL model are explicitly reported. This will allow readers to gauge the reliability of the findings on variables with high and low significance. 5. Recommendations Section Greater Specificity The recommendation for RBI to control money supply is insightful but could be more actionable. Suggest specific measures that RBI could adopt to balance money supply without triggering inflation, perhaps by referencing open market operations or reserve requirements. Expand on your recommendation for export support by discussing concrete steps, such as incentivizing export-oriented sectors or adopting trade policies aligned with ‘Make in India’ and ‘Atmanirbhar Bharat’ initiatives. 6. Address Potential Limitation Variable Selection and Omitted Variables: Mention any limitations related to variable selection, such as the exclusion of additional external factors like global economic events or investor sentiment, which could influence the stock market. Additionally, a brief statement on potential multicollinearity concerns would reassure readers of the model’s robustness. Acknowledge if there are any limitations in the data period that might affect generalizability, especially if there were any structural changes in the economy (e.g., policy changes, COVID-19). Essential Revisions for Scientific Soundness Justify the choice of the ARDL model, including brief references to other possible models. Provide precise details on the diagnostic tests and their results. Expand on limitations related to causality and variable selection. Include significance levels for ARDL coefficients in tables for transparency. Implementing these points will improve the article’s scientific rigour, ensuring that its findings and recommendations are well-supported and its limitations are transparently addressed. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Dr. S. Baranidharan specializes in financial analytics, with a focus on stock market analysis, financial econometrics, and economic modeling. His expertise includes hedging strategies, derivative markets, and the application of econometric methods for financial risk management. Dr. Baranidharan is also well-versed in analyzing macroeconomic variables affecting financial markets, particularly within the Indian context. His research contributes to understanding asset pricing, market efficiency, and the practical applications of quantitative finance. With extensive publications, he actively explores innovations in finance education and data-driven decision-making. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Subburayan B. Reviewer Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r337233 ) The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-337233 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 12 Nov 2024 Sanjay Chauhan , $usrAffiliation 12 Nov 2024 Author Response Respected Sir, With due respect I would like to thank you for your valuable suggestions which I will be incorporating once get access by F1000. Thanks and regards Sanjay ... Continue reading Respected Sir, With due respect I would like to thank you for your valuable suggestions which I will be incorporating once get access by F1000. Thanks and regards Sanjay Singh Chauhan Respected Sir, With due respect I would like to thank you for your valuable suggestions which I will be incorporating once get access by F1000. Thanks and regards Sanjay Singh Chauhan Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 12 Nov 2024 Sanjay Chauhan , $usrAffiliation 12 Nov 2024 Author Response Respected Sir, With due respect I would like to thank you for your valuable suggestions which I will be incorporating once get access by F1000. Thanks and regards Sanjay ... Continue reading Respected Sir, With due respect I would like to thank you for your valuable suggestions which I will be incorporating once get access by F1000. Thanks and regards Sanjay Singh Chauhan Respected Sir, With due respect I would like to thank you for your valuable suggestions which I will be incorporating once get access by F1000. Thanks and regards Sanjay Singh Chauhan Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 30 Oct 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 3 (revision) 03 Jun 25 read read Version 2 (revision) 02 May 25 read Version 1 30 Oct 24 read read read Baranidharan Subburayan , Christ University, Bangalore, India Alireza Askarzadeh , Old Dominion University, Norfolk, USA Hazman Samsudin , The Universiti Malaysia Terengganu, Terengganu, Malaysia Aamir Aijaz Syed , National Institute of Technology, Allahabad, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Samsudin H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Jun 2025 | for Version 3 Hazman Samsudin , The Universiti Malaysia Terengganu, Terengganu, Malaysia 0 Views copyright © 2025 Samsudin H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I now recommend the manuscript to be indexed. Competing Interests No competing interests were disclosed. Reviewer Expertise Finance and International economics, econometrics, development economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Samsudin H. Peer Review Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.183120.r389494) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1299/v3#referee-response-389494 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Aijaz Syed A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Jun 2025 | for Version 3 Aamir Aijaz Syed , National Institute of Technology, Allahabad, India 0 Views copyright © 2025 Aijaz Syed A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I am satisfied with the revised version and approve the version shared. Competing Interests No competing interests were disclosed. Reviewer Expertise Finance and Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Aijaz Syed A. Peer Review Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.183120.r389495) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1299/v3#referee-response-389495 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Aijaz Syed A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 15 May 2025 | for Version 2 Aamir Aijaz Syed , National Institute of Technology, Allahabad, India 0 Views copyright © 2025 Aijaz Syed A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The author should include a clear theoretical contribution in the introduction section. Literature review is adequate however in my opinion author is suggested to include comprehensive literature gap and how the present study fill this gap. The author should highlight the reason for employing the data from 2009 to 2023. Author can add few recent studies: Company-specific antecedents to stock returns: a statistical investigation with special reference of the NIFTY 50. (refer 1 ) Methodology is appropriate however I recommend the author should add few study strengthening the rational for employing ARDL method. Author can refer the following studies: Investigating the impact of the first and second waves of the COVID-19 pandemic on the Indian stock and commodity markets: An ARDL analysis of gold, oil, and stock market prices. (refer 2 ) . The author can strengthen the outcome using empirical literature which is missing Finally, policy specific recommendations need to be added. Best Wishes Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Goel, S., Yadav, N., Wani, N. U. H., & Syed, A. A. (2025). Company-specific antecedents to stock returns: a statistical investigation with special reference of the NIFTY 50. International Journal of Diplomacy and Economy, 11(2), 105-123. 2. Syed, A., Tripathi, R., & Dewan, J. (2021). Investigating the Impact of COVID-19 Pandemic First and Second Wave on the Indian Stock and Commodity Market: An ARDL Analysis of Gold, Oil and Stock Market Price. Syed A. A, Tripathi. R, Dewan Jyoti. Investigating the impact of COVID, 19, 8-21. Competing Interests No competing interests were disclosed. Reviewer Expertise Finance and Economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 11 Aug 2025 Sanjay Chauhan, The above suggestions have been incorporated into version 3 of the article. Thank You View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Aijaz Syed A. Peer Review Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.179731.r382758) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1299/v2#referee-response-382758 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Samsudin H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Mar 2025 | for Version 1 Hazman Samsudin , The Universiti Malaysia Terengganu, Terengganu, Malaysia 0 Views copyright © 2025 Samsudin H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. In the introduction, the author should address why an investigation on stock market is important. Perhaps the author should add some evidence on stock market cause a volatility. this have been documented such as in Hazman (2016)(refer 4), Dai et al., (2020)(refer 3) and Dhingra et al., (2024)(refer 2). Please cite these articles and highlights why it is important to study on stock market. 2. Literature review is fine but can be improve by writing it in the manner of discussions rather than just a list of literature. 3. The method is a bit confusing. The title said a causality investigation and in result the author did mention about Granger causality, but the method used is ARDL. Please note that ARDL is cointegration test and not causality testing. Causality test is more suitable with Granger test and in the case of mixed stationarity as suggested by this study, Toda-Yamamoto causality testing seems more feasible and suitable. So this study investigating causality relationship or cointegration among the variables? please clarify and make it clear. and also are the data is in original form or have been log? 4. The discussion on the results also a bit shallow. please elaborate more. I think it would be beneficial to cite this recent article in 2025 also- refer 1 Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Rahmi N, Samsudin H, Ahmat N: The Dynamic Response of the Green Stock Market to External Economic Policy Uncertainty: The Case of Indonesia. International Journal of Energy Economics and Policy . 2025; 15 (2): 360-369 Publisher Full Text 2. Dhingra B, Batra S, Aggarwal V, Yadav M, et al.: Stock market volatility: a systematic review. Journal of Modelling in Management . 2024; 19 (3): 925-952 Publisher Full Text 3. Dai Z, Zhou H, Wen F, He S: Efficient predictability of stock return volatility: The role of stock market implied volatility. The North American Journal of Economics and Finance . 2020; 52 . Publisher Full Text 4. Samsudin, Hazman & Nawawi, Mohd Nasir & Abdul Halim, Zairihan & Md Said, Ahmad Syahmi, 2018. "Financial Performance Evaluation of Islamic Banking System: A Comparative Study among Malaysia’s Banks," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 137-147. Competing Interests No competing interests were disclosed. Reviewer Expertise Finance and International economics, econometrics, development economics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 09 Aug 2025 Sanjay Chauhan, The above suggestions have been incorporated into version 3 of the article. Thank You View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Samsudin H. Peer Review Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r369212) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-369212 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Askarzadeh A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Mar 2025 | for Version 1 Alireza Askarzadeh , Old Dominion University, Norfolk, Virginia, USA 0 Views copyright © 2025 Askarzadeh A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Summary of the Paper The paper investigates the causal relationship between macroeconomic indicators and stock prices in the Indian stock market using the ARDL bounds testing approach and Granger causality tests. The study analyzes data from April 2009 to March 2023, examining the long-run and short-run effects of variables such as money supply, foreign institutional investment (FII), crude oil prices, exchange rates, and GDP on stock market returns. The findings highlight significant predictive relationships, with policy implications for investors and regulators. While the paper presents a well-structured empirical analysis, there are several areas for improvement, particularly regarding methodological justification, causality interpretation, and the issue of endogeneity. Major Concerns 1. Endogeneity Issues (Reverse Causality & Omitted Variable Bias) The study does not address potential endogeneity concerns, which are critical in empirical finance research. The relationship between macroeconomic indicators and stock prices is likely bidirectional : Reverse Causality: Stock prices themselves influence macroeconomic variables such as exchange rates and FII inflows, making it difficult to establish one-way causation. Omitted Variable Bias: External factors such as global market trends, investor sentiment, or monetary policy shocks could simultaneously affect both stock prices and macroeconomic indicators. Recommendation: The authors should acknowledge this limitation and consider addressing it through: Instrumental Variable (IV) methods (e.g., lagged macroeconomic indicators or global oil price shocks). Structural Vector Autoregression (SVAR) models to capture dynamic interactions. Event study approaches if identifiable policy shocks exist. Without addressing these concerns, the causal claims remain associational rather than truly causal . 2. Justification of Model Choice The use of the ARDL model is reasonable, but the authors should provide a stronger justification for selecting it over other alternatives like VAR or VECM. Specifically: 3. Interpretation of Causality The Granger causality test does not establish true causation but only predictive relationships. This should be made explicit in the discussion. 4. Expanding Literature Comparison The paper would benefit from a stronger comparison with similar studies in emerging markets. Studies from China, Brazil, and South Africa have examined similar relationships and should be referenced. This would help position the paper’s findings within a broader research context. 5. Strengthening Policy Implications The policy recommendations are insightful but remain broad. The authors should: Minor Concerns Presentation of Results : Including impulse response functions or time-series graphs would improve the clarity of findings. Significance Levels in Tables : The authors should explicitly report significance levels (p-values) for each coefficient in the ARDL model. Limitations Section : A dedicated discussion of omitted variables, structural economic shifts (e.g., COVID-19), and potential model improvements should be included. Conclusion This study makes an important contribution to understanding the interaction between macroeconomic indicators and stock prices in India. However, several methodological concerns—particularly regarding endogeneity, causality interpretation, and robustness—should be addressed to strengthen the paper’s scientific rigor. Recommendation: Revision By incorporating these suggestions, the paper will provide a more compelling and reliable contribution to the literature on stock market-macroeconomic interactions. Sincerely, Alireza Askarzadeh Finance Department Old Dominion University Norfolk, VA, USA Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Corporate finance, Investments I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 02 May 2025 Sanjay Chauhan, Respected Sir, I have incorporated most of your suggestions in the revised version. New version includes Impulse response function, limitations and P- Value in ARDL model. Further, I have also expanded the literature review for comparison as suggested. Thanks and Regards Sanjay Singh Chauhan View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Askarzadeh A. Peer Review Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r368214) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-368214 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Subburayan B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Nov 2024 | for Version 1 Baranidharan Subburayan , Christ University, Bangalore, Karnataka, India 0 Views copyright © 2024 Subburayan B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Your analysis provides a strong foundation, examining both short-term and long-term impacts of macroeconomic variables on the Indian stock market. The findings hold significance for investors and policymakers, and you present a clear framework for policy recommendations. However, to further enhance the rigour, clarity, and scientific soundness of your study, consider the following constructive suggestions 1. Clarify the Methodological Approach While using the ARDL model is appropriate, it would be beneficial to justify why this model was selected over other econometric techniques, especially for readers less familiar with time-series methodologies. Could you briefly discuss why ARDL is suitable for analyzing the relationships between non-stationary variables? The diagnostic tests ensure the robustness of the model, yet further clarification on which specific tests (e.g., Breusch-Godfrey for serial correlation or Breusch-Pagan for heteroscedasticity) were used would enhance transparency. Detail the results and thresholds applied to interpret the presence or absence of statistical issues. 2. Strengthen the Discussion on Causality and Interpretation While the Granger causality test is a useful tool for examining predictiveness, it does not confirm causation. A brief statement on this limitation would enhance scientific accuracy, as it would clarify that observed relationships may not imply a causal impact. The findings suggest differing influences of variables like FII and COP in the short vs. long term. A deeper exploration of why these factors have opposing impacts over different time horizons could improve interpretative depth. 3. Expand the Literature Review and Comparison Comparing your findings to those of similar studies in emerging markets or other Asian economies can underscore the unique or common effects of macroeconomic variables in the Indian context. Specifically, mention studies on how money supply, exchange rates, and FII impact stock prices in comparable economies, discussing any consistencies or contradictions. Many of the policy recommendations align with existing government initiatives. Strengthen this section by drawing connections with specific studies or historical examples where similar macroeconomic interventions (e.g., exchange rate management or increased FII) led to observable outcomes. 4. Improve the Presentation of Results Visual representations (e.g., impulse response functions or time-series graphs of each variable’s impact over time) could improve the clarity of your results, especially for capturing short-run vs. long-run effects. Ensure that the significance levels (p-values) of each coefficient in the ARDL model are explicitly reported. This will allow readers to gauge the reliability of the findings on variables with high and low significance. 5. Recommendations Section Greater Specificity The recommendation for RBI to control money supply is insightful but could be more actionable. Suggest specific measures that RBI could adopt to balance money supply without triggering inflation, perhaps by referencing open market operations or reserve requirements. Expand on your recommendation for export support by discussing concrete steps, such as incentivizing export-oriented sectors or adopting trade policies aligned with ‘Make in India’ and ‘Atmanirbhar Bharat’ initiatives. 6. Address Potential Limitation Variable Selection and Omitted Variables: Mention any limitations related to variable selection, such as the exclusion of additional external factors like global economic events or investor sentiment, which could influence the stock market. Additionally, a brief statement on potential multicollinearity concerns would reassure readers of the model’s robustness. Acknowledge if there are any limitations in the data period that might affect generalizability, especially if there were any structural changes in the economy (e.g., policy changes, COVID-19). Essential Revisions for Scientific Soundness Justify the choice of the ARDL model, including brief references to other possible models. Provide precise details on the diagnostic tests and their results. Expand on limitations related to causality and variable selection. Include significance levels for ARDL coefficients in tables for transparency. Implementing these points will improve the article’s scientific rigour, ensuring that its findings and recommendations are well-supported and its limitations are transparently addressed. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Dr. S. Baranidharan specializes in financial analytics, with a focus on stock market analysis, financial econometrics, and economic modeling. His expertise includes hedging strategies, derivative markets, and the application of econometric methods for financial risk management. Dr. Baranidharan is also well-versed in analyzing macroeconomic variables affecting financial markets, particularly within the Indian context. His research contributes to understanding asset pricing, market efficiency, and the practical applications of quantitative finance. With extensive publications, he actively explores innovations in finance education and data-driven decision-making. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 12 Nov 2024 Sanjay Chauhan, Respected Sir, With due respect I would like to thank you for your valuable suggestions which I will be incorporating once get access by F1000. Thanks and regards Sanjay Singh Chauhan View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Subburayan B. Peer Review Report For: A causality investigation into stock prices and macroeconomic indicators in the Indian stock market [version 3; peer review: 2 approved, 2 approved with reservations] . F1000Research 2025, 13 :1299 ( https://doi.org/10.5256/f1000research.172441.r337233) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1299/v1#referee-response-337233 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });