Fed rates and movement of bank stocks in India

Karmakar, Subhash Article Fed rates and movement of bank stocks in India Asian Journal of Economics and Banking (AJEB) Provided in Cooperation with: Ho Chi Minh University of Banking (HUB), Ho Chi Minh City Suggested Citation: Karmakar, Subhash (2025) : Fed rates and movement of bank stocks in India, Asian Journal of Economics and Banking (AJEB), ISSN 2633-7991, Emerald, Leeds, Vol. 9, Iss. 2, pp. 261-283, https://doi.org/10.1108/AJEB-11-2024-0127 This Version is available at: https://hdl.handle.net/10419/334147 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Fed rates and movement of bank stocks in India Subhash Karmakar Department of Management Studies, Bank of India, Kolkata, India Abstract Purpose – The purpose of this paper is to identify the changes in the stock market prices of banks in India with changes in FED rates in long run. Design/methodology/approach – Using both bivariate granger causality and ANOVA along with binomial logistic regression the influence of changes in FED rates on stock market prices of both major public sector and private sector banks have been derived. Findings – Private banks with higher shareholding of Foreign Institutions investors are influenced significantly as compared to public sector banks. Research limitations/implications – The changes in FED rates and its influence over the stock prices are related directly to the shareholding pattern of Foreign Institutional Investors. The results suggest that changes in the FED rates have a direct impact on the financial sector stock prices, where a positive relationship is observed. Practical implications – For investors this provides a decisive signal for market-related investments in public sector vis-avis the private sector banks and for the policy makers this may open the door for policies related to foreign investments as well as policies related to managing liquidity positions. Originality/value – By using real datasets and granger causality analysis this paper will help in understanding the relationship between the banking stocks in India and changes in FED rates. Keywords Fed rates, Granger causality, FDI, Public sector banks, Private banks, Repo rate, Reverse repo Paper type Case study 1. Introduction Interest rates play a key role in the development of the economy and controlling the economic operations of a State. The changes in interest rates provide an important tool through which the central bank in a country takes the lead in the management of inflation as well as market operations and, of course economic growth. Interest rates also impact the flow of credit in the economic system. The movement of interest rates has direct implications for the financial markets including stock markets. In India the Reserve Bank of India (RBI), the Central Bank regulates the economy using the Cash Reserve Ratio (CRR), repo rates, reverse repo rates, Statutory Liquidity Ratio (SLR) etc. The RBI also determines the Government Bond Rates that are floated by the Central and State Governments and provide a good investment opportunity for retail investors as well as for banks and financial institutions to meet SLR requirements. Asian Journal of Economics and Banking 261 JEL Classification — E42, E47, E52, G10, G18, G11 © Subhash Karmakar. Published in Asian Journal of Economics and Banking. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode The authors are sincerely thankful to all anonymous referees whose valuable comments helped to improve the quality of this paper. Declarations: Availability of data and material: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interest: The author declares no competing interest Funding: This research has not received any external funding Authors’ contributions: All authors have equally participated in the research. The author have read and agreed to the final version of the manuscript The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2615-9821.htm Received 22 November 2024 Revised 6 March 2025 7 April 2025 Accepted 7 May 2025 Asian Journal of Economics and Banking Vol. 9 No. 2, 2025 pp. 261-283 Emerald Publishing Limited e-ISSN: 2633-7991 p-ISSN: 2615-9821 DOI 10.1108/AJEB-11-2024-0127 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 Another important feature of the bond market is that it has an inverse relationship with stock prices, so when stock prices rise bond prices fall and vice versa. To meet liquidity requirements in the banking system, commercial banks borrow from RBI by selling their securities to the central bank which is also termed as repo. So, when there is a hike in the repo rate, the cost of borrowing for banks and other financial institutions increases, and it plays a major role in the increasing interest rates for various loans and advances. Similarly, the Federal Fund Rate is set by the Federal Reserve, U.S., which refers to the rate at which commercial banks borrow and lend their excess reserves to each other overnight. By monitoring the FED rates the growth of the economy is monitored, which is decided by the Federal Open Market Committee (FOMC). FED rates are usually reduced when the economy begins to struggle to decrease the borrowing cost. When the economy is over-heated, FED may decide to increase rates, to increase the cost of credit throughout the economy. This change can significantly impact the bond and capital markets. For example when FED rates increase, the stock market may become relatively less attractive. FED rates are also have an impact on stock markets in India, where a rise or decline causes changes in stock markets. When the borrowing rate is low, an investor borrows in the U.S. market and infuses the capital in the Indian market. Conversely, when there is an interest hike that has a positive impact on the U.S. treasury yield, the money moves from Indian markets. The changes in the FED rates do not occur overnight but the news starts moving in the markets months before any announcement and accordingly the equity markets are affected. Foreign direct investment in various sectors in India plays an important role. According to the Invest India site (investindia.gov.in) the total FDI equity inflow during FY: 2022–23 was $70.97bn and the total FDI equity inflows was at $46.03bn. The countries with greatest contributions to FDI equity inflows for FY: 2022–2023 are Mauritius (26%), Singapore (23%), the USA (9%), the Netherlands (7%) and Japan (6%). Amongst, the sector which is having the highest investment in FDI equity inflow during FY: 2022–23 are finance, banking, insurance and other services which accounts for 16% of the total investment. So, it becomes very important to study the impact of FED rates on Banking Sector in India, since it is one of the most preferred sector for investment. India is an attractive destination for foreign investors due to its greater growth in the banking sector, good asset quality etc. According to Islam and Beloucif (2023) the size of a market determines FDI followed by openness, infrastructure quality, labor cost, macroeconomic stability, human capital and the growth prospects of the host country. Doytch (2022) while studying the impact of COVID 19 which caused an economic crisis for a prolonged duration investigated the impact of output growth on acceleration and deceleration. They carried out a study on the FDI data of 34 OECD countries during the period of 1995–2019 and concluded that in financial services FDI inflows are countercyclical, while manufacturing FDI outflows are procyclical. They observed that FDI in transport services is determined by various factors, such as the control of corruption in the home country. FDI in the private banking sector is allowed up to 74% according to the guidelines of RBI and the government and the majority of the investors who hold it are concentrated in India’s largest private sector banks of which the top investee banks are the ICICI Bank, the HDFC Bank and the Kotak Mahindra Bank (S&P Global Market Intelligence 24th September, 2023). Considering the facts and figures it is pertinent that FDI in private Sector banks play a pivotal role in the coming days and it becomes very important to study the impact of changes in FED rates and changes in stock market prices on investor’s sentiments. The purpose of this paper lies in the fact that very few studies are available focusing the impact of changes in FED rates and changes in stock prices of major banking stocks in India, over the long term. Although financial services account for around 36% (As on 31st October, 2023) in terms of weightage in NIFTY 50, which is considered the conglomerate of top 50 stocks according to market capitalization. Moreover, four banks are in the top ten companies and approximately accounting for 27% weightage (As on 31st October, 2023) in terms of market capitalization in NIFTY 50. Therefore, it is of prime importance to study the long -term AJEB 9,2 262 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 impact of changes in the stock prices of the major banking stocks and FED rates in order to decipher the pattern of investments in these stocks influenced by FED rates. The objective of this paper is manifolds; firstly, the paper attempts to reveal the mechanism regarding how changes in FED rates influences the major banking stocks in India on a long run. Secondly, the paper also focusses on the inherent role of FED rates in the foreign direct investments particularly banking stocks. The paper is based on the hypothesis whether any change in the stock market prices follows the change in FED rates or vice versa where in the present context bivariate Granger causality models as substantiated from different studies have been used to determine relationship between changes in interest rates and stock market prices in different categories of Banks. The contributions of this work are as follows. Firstly, though papers are available where stock returns have been correlated to changes in FED rates but their impact on banking stocks particularly in emerging economies like India have been rarely discussed. Moreover, the role of changes in FED rates and their ramifications in the banking sector in India being dominated by the public sector and private sector banks have been discussed. These observations provide an arena for determining the performance of stock returns of the banks with hike in FED rates. Secondly, this paper has made the first attempt to relate how FDI in banks have been playing a major role particularly under a well-regulated financial system where private banks with good performance in terms of parameters are greatly benefited. This may open the doors for FDI in other banks not only to improve the efficiency of the banks but to improve the performance on a global platform to become global banks. So, from an investor’s point of view the role of changes in FED rates are important. 1.1 Primary distinction between this study and prior research According to our survey of the literature, it is found that literature are mainly focused on changes in interest rates and changes in stock market prices or sectoral indices and it is sometimes based on how FED rates impact our economy as a whole. Some of the recent papers (Growing impact of U.S. monetary policy on emerging financial markets-Evidence from India by Lakdawala) pointed out that U.S. monetary policy decisions play a significant role in the Indian Stock markets before the use of unconventional tools and that effects are becoming stronger over time. Apart from these facts, very few studies have investigated the impact of FED rates on banking stocks in India, particularly in long run. There are also few papers related to how foreign institutional investors are being impacted through changes in FED rates. In this context, this paper has made an attempt to bridge the gap in literature where shareholding pattern particularly the shareholding pattern of Foreign Institutional Investors (FII) have been correlated with changes in FED rates. Furthermore, very nascent literature are also available to reveal impact of changes in the FED rates on overall stock markets and in particular financial markets. This paper may be beneficial to policymakers and all stakeholders considering how FED rate interventions may affect financial firms in India and will help in formulation of policies related to FDI and develop a better framework for investment in public sector banks in India to improve the competitiveness and performance of these banks as compared to their private peers. The rest of the paper is organized as follows: section 2 present some of the related literature reviews. Section 3 describes the research framework and methodology, section 4 presents the findings and include relevant discussions. Section 5 highlights the conclusion of this research and policy implications. Table 1 provides the meaning/definitions of some of the terms used in this paper. 2. Related literature The available literature have focused on various methodologies used for deriving the relationship between stock markets and interest rates across different continents. We have classified the literature review into the following sections: Asian Journal of Economics and Banking 263 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 Mukherjee (2007) found that the Indian stock market was having low levels of robustness and efficiency when compared with other stock markets like those in New York, Tokyo and Hong Kong. Gu et al. (2022) were of the view that average interest rates have their negative impact on the returns of the stock market based on the studies on Chinese markets. They suggested that the policymakers should go for marketization of the interest rates. On the same lines, Sahu and Pandey (2020) suggested that in the Indian market, money supply and stock market prices are closely related and having a significant positive impact based on their studies on Indian stock market from 1996 to 2016. While studying the impact of interest rates on the stock markets Ramsharan (2019) found that they were inversely related. Batrancea et al. (2021) while studying the economic growth of African countries suggested that economic growth is proxied by GDP growth rates and other indicators like imports, exports and gross domestic savings. Ugurlu et al. (2021) while studying the relationship among investor’s sentiments, monetary policies and stock markets in the U.S. derived a significant negative form in terms of uncertainty in monetary policies. Kim (2023) identified that a sharp hike in the FED rates leads to movement of investors in the emerging markets. Kaur et al. (2023) while working on BRICS economy noted that foreign investments, commodity prices and money supply are the key factors for greener growth in BRICS economies. Hillier and Loncan (2019) noted that a market integration improves the well-governed firms. Acharya et al. (2020) investigated the transmission of Central Bank’s liquidity to Table 1. Definitions/descriptions of the terms Term Definitions/Descriptions FED Rates Federal Funds Rate is set by the Federal Reserve, U.S. which refers to the rate at which commercial banks borrow and lend their excess reserves to each other overnight Granger Causality The Granger causality is defined in terms of whether historical information set on a variable can predict another variable Foreign Direct Investment (FDI) Foreign Direct investment is the investor of a foreign company/firm or ownership in a company which is of other country Public Sector Banks (PSBs) Public Sector Banks are those banks where majority ownership lies with the state Private Sector Banks Private sector Banks are those banks where majority of the stake is held by individuals/groups Cash Reserve Ratio(CRR) It is the percentage of the certain position of deposits which are to be maintained with the Central Bank in order to maintain the liquidity position of the banks SLR(Statutory Liquidity Ratio) It is the investment that a bank makes in liquid assets like Government Securities (G-Sec), gold instead of cash to maintain the solvency of the banks Repo Rate(Repurchase Rate) It is the rate which financial institutions pays to the Central Bank/Reserve Bank of India for loans secured by securities. The RBI changes the rates in order to regulate the interest to be charged in loans and deposits and influences the borrowing cost of banks Reverse Repo Rate It is the rate which is paid by the Central Bank/Reserve Bank Of India when banks park their excess liquidity/money with RBI Nifty 50 The Nifty 50 is a diversified 50 stock index accounting for 13 sectors of the economy. The Nifty 50 Index represents about 59% of the free float market capitalization of the stocks listed on NSE(National Stock Exchange) as on September 29, 2023.(Source: nse.india.com) Nifty Bank Index It refers to the diversified stock index of Banks (Public Sector Banks and Private Sector Banks) Source(s): Table by author AJEB 9,2 264 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 bank deposits and loan spreads in Europe for the period of January 2006 to June 2010 and were of the view that during easing of the monetary policies puts constraints on the banks capital base. Conrad (2021) while studying the impact of impact of interest rates and monetary policy on the stock market noted that any decrease of interest rates may have a direct impact on the share prices of the firm. Their conclusions were in line with extreme expansive monetary policy with low, zero or negative interest rates encourage financial bubbles on the stock market. Hirota (2023) while working on how the money supply in the economy influences demand for stocks found that during the COVID phase the changes in money supply and monetary policy may have the disconnect between stock market and real economy. Thorbecke (2023) while working during the same period noted that changes in monetary policies by the FED had influenced on the stock markets and in particular the volatility of the stock markets increased during the pandemic period with the changes in policies. Garga et al. (2022) investigated the inflation targeting of Reserve Bank of India (RBI) during 2015 and how the market perceived such changes in the monetary policies have been discussed. On the same lines again Lakdawala et al. (2023) investigated how the bond market responded to Reserve Bank of India’s monetary policy at the start of pandemic and they noted that due to measures taken by RBI the interest rates in long term bond markets had remained high during the early phase of the COVID 19 pandemic. Kedia and Vashisht (2017) while deciphering the relation between interest rates and stock markets stressed upon the fact that there is no relationship regarding inflation, policy interest rates and exchange rates. Kumuda et al. (2016) analyzed the relationship between interest rates and stock prices in the context of India for a 10 year period from 2005 to 2014 where they observed that six sectors (auto, bank, FMCG, financial services, IT and Pharma) out of 11 sectors were significantly impacted by the policy interest rates. Batrancea (2021) while studying the impact of financial performance on the assets and liabilities of banks in Europe during 2006–2020 recorded that there is a strong impact on the banks assets and liabilities related to financial indicators. Lakdawala (2021) while studying the impact of US monetary policies on the Indian stock market derived significant effects which have become robust over the period of time. They also found that the decisions regarding investments were mainly driven by the foreign institutional investors, which also play an important role in the stock market. Kaur et al. (2021) identified various literature related to study of macroeconomic variables on the economic growth of BRICS countries where GDP has been proxied as measure of economic growth; to study the macroeconomic variables affecting the stock market of BRICS nations through content analysis. The study has helped to fulfill the research gaps that confine the influence of macroeconomic variables and restrict the growth of BRICS countries. Guo et al. (2022) concluded that impact of Federal funds rate (FFR) is mostly powerful when sentiment-driven overvaluation is followed by a correction. They suggested that monetary easing surprises during sentiment-waning phases boost the stock market by alleviating investors’ fear. Balci et al. (2022a) used geometric coarse graining method on stock market indices to show changes across the markets during three financial crisis periods. Balci et al. (2022b) used network induced soft sets to investigate the efficacy of these methods during various economic stress periods to study the interaction in stock markets. Ni (2023) while studying the impact of increase in the FED rates noted that any increase in the rates had a negative impact on the economic activities and investments moves out from foreign countries. So, if the FED rates remain increasing the foreign countries may compensate the outflows through changes in their monetary policies. Batrancea et al. (2023) while studying corruption perception index impacts the countries and their boundaries. Asian Journal of Economics and Banking 265 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 2.1 Empirical literature and hypotheses development In order to define the relationship between the policy interest rates, inflation, exchange rates and money supply or other factors which emanate from changes in monetary policies and the stock markets various authors have adopted various methodologies like Granger Causality models, Johansen`s approach of co-integration and Toda and Yamamoto Granger causality test etc. Depending upon the nature of the study and type of data co-integration methods have been used the most but Granger causality models have also yielded good results. Some of the papers have relied upon the autoregression models to have significant results. Various authors have used Bayesian regression models as well as some have used panel cointegration tests for good results. First of all it is required to establish the nature of the variables, depending upon various test results like autocorrelation test and multicollinearity tests, proper methods for further analysis are to be selected. In this paper, the combination of binomial regression and cointegration tests have been carried out in order to establish the results which have been obtained through ANOVA. Since, ANOVA results may not be as useful and robust as compared to binomial regression based on proportions or probability and cointegration methods, both these tests have been carried out. Furthermore, to check robustness of the results omnibus likelihood tests have been also carried out so find out the variables which are important. The details of methodologies adopted in various papers may be summarized as: If we take a closer look at Table 2 it is envisaged that modern papers rely upon not only the cointegration techniques but also on the probability based regression models. So, considering the various models proposed by various authors, in this paper Granger Causality model along with panel cointegration methods have been used. Apart from cointegration models, binomial logistic regression has been used being a probability-based approach. 3. Methodology The Methodology used in paper may be depicted in form of flowchart as Figure 1 (By Author). Based on the literature reviews, Bivariate Granger Causality model with Box and Cox transformation have been used after Binomial logistic regression. The Durbin and Watson’s statistic have been calculated with p values to detect whether any autocorrelation exists in the data or not. The detailed discussions on the methodology used and its implications are elaborated in this section. 3.1 Granger’s causality The Granger causality is defined in terms of whether historical information set on a variable can predict another variable. In theory, the traditional Granger causality test is a joint restriction test and strictly relies on the assumption that the underlying data generating process is stationary. Correlation may be defined as the mutual relationship between two variables or two or more processes based and it is not expected by chance alone. Causality which may be referred to cause and effect, is the relationship between two processes, the first of which (the cause) is partially or totally responsible for the second, while the second is partially or totally dependent on the first. (Rossi, 2013). The causal effect of two variables X and Y can be determined with the use of the Granger causality test, named after the British econometrician Sir Clive Granger. This test makes use of Student’s t-statistic and F-statistic tests and identifies whether the values of the variable X provide statistically significant information about the evolution of the future values of the variable Yassuming that X and Yare having stationary time series of data (Stationarity in time series data refers to the fact that statistical properties of the time series do not change over time). The Granger-causality tests assess the significance of the proposed predictors in a regression of the dependent variable (y t þ h ), onto the lagged predictors (x t ), where h is the AJEB 9,2 266 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 forecast horizon. The Granger-causality test is a simple F-test on the parameter vector α h where: y tþh ¼ α 0 h x tþβ 0 h z tþ ε tþh ; where t ¼ 1; ...::; T and Zt are control variables with lags of y : yt; yt þ 1 It is assumed that the regressors are suitable predictors when the statistical tests reject the null hypothesis that the regressors are insignificant (i.e., when the F-test for testing the hypothesis α h 5 0 rejects at standard significance levels). 3.1.1 The stationarity in the time series data can be analyzed with the help of two important statistical tests. (1) Augmented Dickey Fuller Test (ADF): The Dickey Fuller Test (DF) is used to determine where any autoregressive model is having a unit root which implies that the data is not stationary. The null hypothesis of DF test is that there is a unit root in the time Table 2. Methodologies in various papers Name of the authors Year Model used and period of study Purpose of study Results Sampene et al. 2021 Johansen Multivariate Cointegration Method (2000–2019) Relationship between Interest rate, rate of Inflation, Exchange rate and Money Supply and stock market index Significant long run cointegration among interest rates and Ghana Composite stock index Chaudhary, Bakhshi and Gupta 2020 Generalized Autoregressive Conditional Heteroscedasticity (GARCH) (January–June2020) Impact of COVID 19 on return and volatility of stock market Positive and significant impact found in all the indices in the market Ahmed 2008 Johansen`s approach of co-integration and Toda and Yamamoto Granger causality test (March 1995–March 2007) Relationship between stock market prices and key macroeconomic variables Interest rate seems to lead stock prices Aggrawal and Agarwal 2017 Granger Causality Model and Johansen`s approach of cointegration test(January 2008 to December 2013) Relationship between interest rates and stock markets Interest rates and stock markets move together in long run Yadav et al. 2021 Vector Error Correction Model and Cointegration test (2000–2020) Relationship between stock market prices and key macroeconomic variables There is a long run association between macroeconomic variables and stock markets Sun and Yang 2018 Markov switching vector auto regression (MS-VAR) model Identification of asymmetric effects of China’s monetary policies on stock markets China’s monetary policy has stronger effects on the bull market than bear market William M. Briggs 2023 Bayesian modelling Classical economic problems Partial solution to replication crisis Anita Kalia 2024 Bayesian regression Relationship between promoter’s share pledging and company’s dividend payout policy Significant negative association promoter’s share pledging and company’s dividend payout policy Source(s): Table by author Asian Journal of Economics and Banking 267 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 series data which implies that the data series is not stationary. The alternative hypothesis is that the data is stationary. (2) Phillips–Perron Unit Root Tests (PP) The Phillips-Perron (PP) unit root test mainly deals with serial correlation and heteroskedasticity in the errors. In particular, where the ADF tests use a parametric auto regression to approximate the ARMA structure of the errors in the test regression, the PP tests ignore any serial correlation in the test regression. Hypothesis: Ho: The process has a unit root. Carrying Out ANOVA and Binomial Regression Stationarity of data Significant Non-Significant Carrying out Granger Causality Test with hypothesis framing for prediction of whether Independent variable can be predicted with time lags for dependent variable. Calculation of quarterly changes in FED rates and Average of closing stock prices in %. Framing of Hypothesis for ADF Test, PP test and KPSS Test to find out whether the FED rates in % are forming stationary data series. Reject Null Hypothesis Accept Null Hypothesis Interpretation of results Collection of the Monthly FED Rates from FRED Economic Data and the Closing Stock Prices of Banks (11 PSBs and 13Pvt.Banks) from July2007December 2021 Figure 1. Outline of paper. Source: By author AJEB 9,2 268 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 As the computed p-p-value is lower than the level of significance of 0.05, so we reject the null hypothesis and accept the alternative hypothesis in case first and third cases except in cases where r 5 1. In the third case, the null hypothesis is rejected and we consider the time series as stationary. So, considering the above test results it may be considered that the data has stationarity but the Granger causality test particularly the bivariate Granger causality test has certain limitations as discussed in Para 3.3. Furthermore, to confirm the results binomial logistic regression has been also carried out. First, Analysis of variance (ANOVA) is carried out to determine the relationship between percentage changes in closing prices of banking stocks and percentage changes in FED rates. The ANOVA results have been presented in Table 9. From the table, it is observed that a significant relationship between the FED rate and Closing price exists in the case of City Union Bank, Federal Bank, ICICI Bank, HDFC Bank, IndusInd Bank, Karur Vysya Bank, Kotak Mahindra Bank and Yes Bank where all of these are private banks. No significant relationship exists in the case of Public Sector Banks (PSB). The FII shareholding in these banks has been 27.40% in City Union Bank, 26.30% in Federal Bank, 49.20% in HDFC Bank, 45.70% in ICICI Bank, 24.70% in IndusInd Bank, 14.80% in Karur Vysya Bank, 32.50% in Kotak Mahindra Bank and 26.70% in Yes Bank (As of February 2025). The R-squared value and Adjusted R-square value have been worked out along with the F-Statistics. So, from the ANOVA table, we conclude that the following banks have a significant relationship: City Union Bank, Federal Bank, ICICI Bank, HDFC Bank, IndusInd Bank, Karur Vysya Bank, Kotak Mahindra Bank and Yes Bank. But, since multicollinearity and autoregression are present in the data we go for binomial logistic regression to confirm the results of ANOVA. So, secondly, binomial regression is carried out to confirm the results of the ANOVA, since binomial regression is a probabilitybased test where results are based on two outcomes whether there is a rise or fall in the percentage of closing prices of bank stocks with a rise or fall in FED rates, which is to be tested. In the present case since it is observed that autocorrelation and multicollinearity exist in the data, to get better results and to check the robustness of the results, further the omnibus likelihood test is carried out, to determine how the variables (percentage change in closing prices) are being influenced by fall or a rise in FED rates. The binomial regression has been carried out with the percentage fall in closing prices vis- -a-vis percentage rise in closing prices (individual banking stocks) have been assigned values “2” and “1” respectively, in a similar manner the percentage fall or rise in FED rates have been assigned values “3” and “4”.The overall percentage quarterly fall and rise in closing prices(including all the bank stocks under Table 8. Results of the KPSS test KPSS test (Level/Lag short/N) KPSS test (Trend/Lag short/N (r 5 1)) KPSS test (Trend/Lag short/N0.1*t) Eta (Observed value) 0.056 0.164 0.052 Eta (Critical value) 0.451 0.145 0.145 p-value (one-tailed) 0.872 0.030 0.616 alpha 0.05 0.05 0.05 Test interpretation H0: The series is stationary Ha: The series is not stationary Source(s): Table by author Asian Journal of Economics and Banking 275 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 study quarterly) have been assigned the values “5” and “6”. To confirm these results of the omnibus likelihood test, the Granger Causality Test has been conducted. The Granger Causality test has been carried out considering the heterogeneity in the data. Similarly, in the case of binomial regression, the dependent variable is the percentage rise or fall in closing prices and the factors are the percentage fall or rise in closing prices of individual financial institutions/banks as well as the percentage fall or rise in FED rates with their assigned values as discussed earlier in the text. The results of binomial logistic regression with omnibus likelihood test results have been placed in Table 10 (separately for Public Sector Banks and Private Banks). The results of binomial logistic regression and omnibus likelihood (Table 11 for private banks and Table 12 for public sector banks) test confirm that in the case of Public Sector Banks except for the State Bank of India (SBI), none of the other banks are not influenced by any changes in the FED rates. State Bank of India is the largest bank in India with the largest asset size. The shareholding of Foreign Institutional Investors (FII) in SBI as of December 2024 has been 10.3% and the government shareholding has been also lowest to the extent of around 57% (As of June 2024). On the contrary, the private bank’s results differ, where Federal Bank, IDFC Bank, IndusInd Bank and Kotak Mahindra Bank are being influenced by changes in FED rates. The FII shareholding of almost all of these banks ranges between 25% and 32% (As of February 2025). Table 9. Results of regression between changes in FED rates and changes in closing prices ` Name of the bank FII (Foreign institutional investors) Shareholding in %(as of Feb,2025) Type of bank p-value for regression between changes in FED rates and closing prices(Omnibus ANOVA Test) (Level of significance: 0.05) R-square value Adj. R-square F-test value 1 Bank of Baroda 8.90% PSB 0.736 0.790 0.782 103.00 2 Bank of India 2.92% PSB 0.187 0.631 0.617 46.90 3 Bank of Maharashtra 1.54% PSB 0.389 0.268 0.241 9.91 4 Canara Bank 11.10% PSB 0.725 0.803 0.796 112.00 5 Central Bank Of India 0.44% PSB 0.210 0.088 0.052 2.44 6 Indian Bank 4.78% PSB 0.900 0.037 0.002 1.01 7 Indian Overseas Bank 0.02% PSB 0.495 0.523 0.506 30.20 8 Punjab National Bank 5.70% PSB 0.152 0.677 0.665 57.60 9 State Bank Of India 10.30% PSB 0.073 0.091 0.057 2.70 10 UCO Bank 0.02% PSB 0.953 0.490 0.472 26.40 11 Union Bank of India 6.46% PSB 0.188 0.090 0.055 2.53 12 Axis Bank 47.30% Pvt. Bank 0.609 0.128 0.094 3.76 13 City Union Bank 27.40% Pvt. Bank 0.001 0.625 0.611 45.80 14 DCB Bank 10.86% Pvt. Bank 0.438 0.017 0.019 0.470 15 Federal Bank 26.30% Pvt. Bank 0.009 0.802 0.795 111.00 16 ICICI Bank 45.70% Pvt. Bank 0.001 0.723 0.713 71.70 17 HDFC Bank 49.20% Pvt. Bank 0.028 0.654 0.642 52.10 18 IDFC Bank 27.10% Pvt. Bank 0.085 0.730 0.721 74.50 19 IndusInd Bank 24.70% Pvt. Bank 0.010 0.212 0.182 7.24 20 Karur Vysya Bank 14.80% Pvt. Bank 0.014 0.603 0.589 41.80 21 Kotak Mahindra Bank 32.50% Pvt. Bank 0.010 0.520 0.503 29.30 22 South Indian Bank Ltd 11.46% Pvt. Bank 0.187 0.564 0.548 35.60 23 Yes Bank 26.70% Pvt. Bank 0.028 0.289 0.263 11.20 Source(s): Table by author AJEB 9,2 276 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 Table 10. Binomial logistic regression results Model fit measures for public sector banks (binomial logistic regression) Overall model test Model Deviance AIC BIC R 2 McF χ 2 df p 9.67E�10 26 51.6 1 73.3 12 <0.001 Model fit measures for private banks (binomial logistic regression) Overall model test Model Deviance AIC BIC R 2 McF df p 1 16.8 44.8 72.7 0.774 13 <0.001 Source(s): Table by author Table 11. Omnibus likelihood ratio tests (private banks) Predictor χ 2 df p % change in FED rates (2) 0.8033 1 0.37 Axis Bank 0.5367 1 0.464 City Union Bank 2.2134 1 0.137 DCB Bank 3.0123 1 0.083 Federal Bank 3.936 1 0.037 ICICI Bank 1.3704 1 0.242 HDFC Bank 2.66 1 0.103 IDFC Bank 5.4534 1 0.02 IndusInd Bank 6.774 1 0.009 Karur Vysya Bank 3.4868 1 0.062 Kotak Mahindra Bank 4.7173 1 0.03 South Indian Bank 0.0282 1 0.867 Yes Bank 0.23 1 0.631 Source(s): Table by author Table 12. Omnibus likelihood ratio tests (public sector banks) Predictor χ 2 df p % change in FED rates (2) 2.78E�13 1 1 Bank of Baroda 1.75E�12 1 1 Bank of India 2.42E�12 1 1 Bank of Maharashtra 9.72E�11 1 1 Canara Bank 6.77E�11 1 1 Central Bank of India 6.52E�11 1 1 Indian Bank 9.75E�11 1 1 Indian Overseas Bank 9.66E�11 1 1 Punjab National Bank 1.33E�12 1 1 State Bank of India 12 1 <0.001 UCO Bank 7.97E�10 1 1 Union Bank of India 1.42E�10 1 1 Source(s): Table by author Asian Journal of Economics and Banking 277 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 To confirm the ANOVA results as well as the binomial regression results bivariate Granger Causality test has been carried out whose details have been discussed in the following paragraphs. We present the Granger’s Causality Test results for different Banks considering the following hypothesis: H0. Any change in the quarterly FED rates (X) either a rise or fall does not granger the quarterly change in Closing stock prices (Y) in % either a rise or a fall. H1. Any change in the quarterly FED rates (X) either a rise or fall does granger the quarterly change in Closing stock prices (Y) in % either a rise or a fall. The results of the Bivariate Granger Causality Test with Box and Cox transformation and nonseasonal lag of 1 are presented in the following Table 13. We have carried out a Bivariate Granger Causality test using the URL http://www.wessa.net/ rwasp_grangercausality.wasp/with R –Codes. The box and cox transformation has been carried out in the X series and Y series. The non-seasonal time lags are 1 in the test. From the table, it is amply clear that a significant relationship is there in the case of Private Banks like Axis Bank, City Union Bank, DCB Bank Ltd., HDFC Bank, Karur Vysya Bank and Kotak Mahindra Bank. If we take a closer look at all the banks the FII shareholding pattern has been also higher in this group as compared to other banks. So, from the results of binomial regression and Bivariate Granger causality, we infer that the following are the banks where any quarterly changes in FED rates have a significant impact on changes in stock prices quarterly: (1) Federal Bank (2) ICICI Bank Table 13. Results of the bivariate granger causality test S No Banks/Fis Type of banks Nifty F-test value Y 5 f (X) F-test value X 5 f (Y) 1 Bank Of Baroda PSB Nifty Bankex 3.46 0.558 0.35 0.068 2 Bank Of India PSB 0.76 0.388 1.19 0.281 3 Bank of Maharashtra PSB 0.00 0.953 0.38 0.751 4 Canara Bank PSB 0.09 0.767 0.83 0.368 5 Central Bank PSB 0.67 0.418 0.02 0.892 6 Indian Bank PSB 1.62 0.209 2.00 0.163 7 Indian Overseas Bank PSB 0.03 0.862 2.59 0.114 8 Punjab National Bank PSB Nifty Bankex 0.03 0.866 0.65 0.422 9 State Bank of India PSB Nifty Bankex/Nifty 50 0.88 0.353 3.34 0.073 10 UCO Bank PSB 1.32 0.255 0.10 0.748 11 Union Bank Of India PSB 0.00 0.992 0.92 0.343 12 Axis Bank Pvt Bank Nifty Bankex/Nifty 50 0.90 0.347 8.20 0.006 13 City Union Bank Pvt Bank 1.67 0.202 6.11 0.017 14 DCB Bank Ltd Pvt Bank 0.06 0.806 4.64 0.036 15 ICICI Bank Pvt Bank Nifty Bankex/Nifty 50 1.13 0.292 1.08 0.303 16 HDFC Bank Pvt Bank Nifty Bankex/Nifty 50 0.06 0.810 8.13 0.006 17 IDFC Bank Pvt Bank Nifty Bankex 0.03 0.859 1.67 0.202 18 Karur Vysya Bank Ltd Pvt Bank 0.49 0.486 4.33 0.042 19 Kotak Mahindra Bank Pvt Bank Nifty Bankex/Nifty 50 0.74 0.394 9.93 0.003 20 South Indian Bank Pvt Bank 0.01 0.941 3.21 0.079 21 Yes Bank Pvt Bank 0.12 0.727 3.04 0.087 Source(s): Table by author AJEB 9,2 278 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 (3) IndusInd Bank (4) IDFC Bank (5) Axis Bank (6) City Union Bank (7) DCB Bank Ltd. (8) HDFC Bank (9) Karur Vysya Bank (10) Kotak Mahindra Bank. (11) State Bank Of India If we recollect the facts regarding FII in banks which have been discussed in Table 9 is strong enough to predict that stock market prices of banks with the highest FDI are being affected by the quarterly changes in FED rates. On the contrary, since in PSBs, the FDI is meager FED rates fail to predict the stock market prices in that way as in the case of the largest Private Banks. The observations are also favorable for some of the well-managed and well-capitalized banks that direct the NIFTY Bankex and the overall momentum of the financial market since the majority of the banks are in NIFTY 50. Furthermore, in order to delve into the relationship between the changes in the FED rates and closing stock prices, the probabilities of rise in the closing prices with changes in FED rates have been calculated. The results are presented in the following table. Table 14 represents the probabilities of percentage rise in closing prices with hike or fall in the FED rates. It is observed that there has been 67% rise in closing prices with hike in FED rates and 33% rise in closing prices with fall in FED rates. 4.3 Discussions The results have been summarized as: (1) In case of changes in FED rates, quarter-wise has direct implications on the quarterwise changes in the stock prices of banking stocks particularly the banks having higher market capitalization. Only those banks that have moderate to high Foreign Direct Investment in their capital structure are having an impact. So, in the long run, the four largest private banks operating in India viz. ICICI Bank, Axis Bank, Kotak Mahindra Bank and HDFC Bank’s quarterly average stock prices are influenced by the changes in FED rates. (2) It is observed that stock prices of PSBs except State Bank of India are not influenced by changes in FED rates in the long run and they are totally out of the herd. So, in the case of India, the ownership structure of financial institutions particularly the shareholding of Financial Institutional Investors (FIIs) do have a vital role to play in case of stock Table 14. Estimated marginal means of rise in closing prices 95% confidence interval Change in FED rates Probability SE Lower Upper Rise in FED rates 0.67 0.0821 0.492 0.805 Fall in FED rates 0.33 0.0993 0.263 0.634 Source(s): Table by author Asian Journal of Economics and Banking 279 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 market prices in the long run since FDI norms are framed with certain restrictions in investment in PSBs. (3) Our results also suggest that average quarterly changes in the stock prices of the major banks are influenced by changes in FED rates. So, it is very prominent that changes in FED rates have a profound influence on changes in the stock market prices and any change may predict the future prices of Banks that have a good amount of foreign investment. 5. Conclusion 5.1 Theoretical implications and policy implications The implications may be summarized as: (1) Quarterly Stock prices of new-generation Private Banks which are well-capitalized are influenced by quarterly changes in FED rates. So, well-managed and well-capitalized new-age technology-driven private banks having the major chunk of the market share are of prime importance which are affected by changes in FED rates. Here, the changes are related directly to the shareholding pattern where investment by Foreign Institutional Investors is important. If put in another way, investors are always interested in those banks that are paying good dividends which in turn is related to the performance of the banks. Furthermore, the results suggest that changes in the FED rates have a direct impact on the financial sector stock prices, where a positive relationship is observed. This positive relationship is based on the theory proposed by Keynesian economists where revenue effects dominate over the cost effects (Garg, 2008). (2) In the studies, it is observed that ownership structure has a role to play particularly state ownership of financial institutions. Limited studies have been carried out to find out this theory of how the ownership structure of financial institutions has an impact on the stock markets concerning FED rates. The domino effects of changes in the FED rates are reflected in the interest rates and borrowing costs of financial institutions and a sound financial institution. (3) The FDI has increased manifolds in the last couple of years, particularly in Private Sector Banks where the policymakers have allowed up to 74% FDI and the results are very much evident in our studies where investors are investing through various channels in stable and well-capitalized banks. The policymakers particularly the Reserve Bank of India and other regulators are to make decisions to open doors for FDI in the public banking sector to improve the competitiveness in these banks which may also attract FDI shortly at par with their private sector counterparts. 5.2 Managerial and policy contributions (1) When we compare the changes in stock market prices of various stocks quarterly with quarterly changes in FED rates, it is observed that the movement of some of the private bank stocks may be predicted from changes in FED rates inter alia changes in the shareholding pattern. So, from an investor’s viewpoint, this is very important when one may make a judicious decision on having long-term returns from changes in the FED rates and FII investment patterns since the investment patterns are directly correlated to the performance of the banks. Investment managers dealing in investments on behalf of their clients may use these patterns as observed in this paper to take informed decisions based on changes in FED rates. (2) When we compare the changes in the FED rates and Stock prices we observe that those banks where FII shareholding is highest are being influenced much. It is also important AJEB 9,2 280 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 to note that prime-performing banks with a good capital base have been able to attract foreign investors and influence the market in long run as compared to others. These results may be pertinent to policy makers, where they may modify policy for investment in public sector banks as well as FDI percentage. 5.3 Limitations and future research perspectives The limitation of the paper lies in the fact that we have compared the data of banks only with FED rates, but there are also some good NBFCs (Non-Banking Financial Companies) and insurance companies which may be also compared. Further research work may be carried out to find out how the changes in FED rates on short run affects the closing prices of the stock. Future Scope lies in the fact how the changes in FED rates are affecting the mutual fund market from where we may get a viewpoint for investors at large. References Acharya, V.V., Imbierowicz, B., Steffen, S. and Teichmann, D. (2020), “Does the lack of financial stability impair the transmission of monetary policy?”, Journal of Financial Economics, Vol. 138 No. 2, pp. 342-365, doi: 10.1016/j.jfineco.2020.06.011. Balcı, M.A., Batrancea, L.M., Akg€ uller, € O. and Nichita, A. (2022a), “Coarse graining on financial correlation networks”, Mathematics, Vol. 10 No. 12, 2118, doi: 10.3390/math10122118. Balcı, M.A., Batrancea, L.M. and Akg€ uller, € O. (2022b), “Network-induced soft sets and stock market applications”, Mathematics, Vol. 10 No. 21, 3964, doi: 10.3390/math10213964. Batrancea, L.M. (2021), “An econometric approach on performance, assets, and liabilities in a sample of banks from Europe, Israel, United States of America, and Canada”, Mathematics, Vol. 9 No. 24, 3178, doi: 10.3390/math9243178. Batrancea, L., Rathnaswamy, M.M. and Batrancea, I. (2021), “A panel data analysis of economic growth determinants in 34 African countries”, Journal of Risk and Financial Management, Vol. 14 No. 6, p. 260, doi: 10.3390/jrfm14060260. Batrancea, L.M., de Jes� us Bello G� omez, F., Nichita, A. and Dragolea, L.L. (2023), “Crunching numbers in the quest for spotting bribery acts: a cross-cultural rundown”, in McGee, R.W. and Benk, S. (Eds), The Ethics of Bribery, Springer, Cham, pp. 329-343, doi: 10.1007/978-3-03117707-1_19. Bernanke, B.S., Boivin, J. and Eliasz, P. (2005), “Measuring the effects of monetary policy: a factoraugmented vector autoregressive (FAVAR) approach”, Quarterly Journal of Economics, Vol. 120 No. 1, pp. 387-422, doi: 10.1162/0033553053327452. Chen, B., Qin, J. and Yuan, Ao (2022), “Variable selection in the Box–Cox power transformation model”, Journal of Statistical Planning and Inference, Vol. 216, pp. 15-28, ISSN 0378-3758 doi: 10.1016/j.jspi.2021.05.003. Conrad, C. (2021), “The effects of money supply and interest rates on stock prices, evidence from two behavioral experiments (february 23, 2021)”, Applied Economics and Finance, Vol. 8 No. 2, available at: https://ssrn.com/abstract53791211 Doytch, N. (2022), “FDI: hot or cold money? The behaviour of sectoral FDI inflows and outflows over periods of growth accelerations and decelerations”, Foreign Trade Review, Vol. 57 No. 3, pp. 324-350, doi: 10.1177/00157325221092614. Garg, K. (2008), “The effect of changes in the federal funds rate on stock markets: a sector-wise analysis”, Undergraduate Economic Review, Vol. 4 No. 1, available at: https:// digitalcommons.iwu.edu/uer/vol4/iss1/2 Garga, V., Lakdawala, A. and Sengupta, R. (2022), “Assessing Central Bank Commitment to inflation targeting: evidence from financial market expectations in India”, Federal Reserve Bank of Boston Research Department Working Papers, doi: 10.29412/res.wp.2022.19. Gonçalves, R. (2023), “Exact maximum likelihood estimation of the box-cox transformation parameter”, AIP Conference Proceedings, Vol. 2879, 060001, doi: 10.1063/5.0175370. Asian Journal of Economics and Banking 281 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 Gu, G., Zhu, W. and Wang, C. (2022), “Time-varying influence of interest rates on stock returns: evidence from China”, Economic Research-Ekonomska Istra� zivanja, Vol. 35 No. 1, pp. 25102529, doi: 10.1080/1331677X.2021.1966639. Guo, H., Hung, C.-H.D. and Kontonikas, A. (2022), “The Fed and the stock market: a tale of sentiment states”, Journal of International Money and Finance, Vol. 128, 102707, doi: 10.1016/ j.jimonfin.2022.102707. Hillier, D. and Loncan, T. (2019), “Stock market integration, cost of equity capital, and corporate investment: evidence from Brazil”, European Financial Management, Vol. 25 No. 1, pp. 181-206, doi: 10.1111/eufm.12147. Hirota, S. (2023), “Money supply, opinion dispersion, and stock prices”, Journal of Economic Behavior and Organization, Vol. 212, pp. 1286-1310, doi: 10.1016/j.jebo.2023.06.014, available at: https://www.sciencedirect.com/science/article/pii/S0167268123002202 Islam, M.S. and Beloucif, A. (2023), “Determinants of foreign direct investment: a systematic review of the empirical studies”, Foreign Trade Review, pp. 309-337, doi: 10.1177/00157325231158846. Kaur, S., Aggarwal, S. and Garg, V. (2021), “A study of macroeconomic effects on the growth of BRICS: a systematic review”, International Journal of Economic Policy in Emerging Economies, Vol. 1 No. 1, p. 1, doi: 10.1504/ijepee.2021.10042091. Kaur, S., Aggarwal, S. and Sarwar, S. (2023), “Trade balance, monetary supply, commodity prices, and greener energy growth: contextual evidence from BRICS economies in the lens of sustainability”, Environmental Science and Pollution Research International, Vol. 30 No. 29, pp. 73928-73940, doi: 10.1007/s11356-023-27475-3. Kedia, N. and Vashisht, A. (2017), “An empirical study of relation between indian stock market and inflation, interest rates and exchange rates”, GE-International Journal of Management Research, Vol. 5 No. 4, pp. 2321-1709. Kim, J. (2023), “Stock market reaction to US interest rate hike: evidence from an emerging market”, Heliyon,Vol. 9 No. 5, e15758, doi: 10.1016/j.heliyon.2023.e15758, available at: https:// www.sciencedirect.com/science/article/pii/S2405844023029651 Kumuda, P.R., Mahesh, K.G. and Sirisha, J. (2016), “Interest rate and stock prices – evidence from India”, Research Journal of Finance and Accounting, Vol. 7 No. 21. Lakdawala, A. (2021), “The growing impact of US monetary policy on emerging financial markets: evidence from India”, Journal of International Money and Finance,Vol. 119, 102478, doi: 10.1016/j.jimonfin.2021.102478, available at: https://www.sciencedirect.com/science/article/pii/ S0261560621001297 Lakdawala, A., Pratap, B. and Sengupta, R. (2023), “Impact of RBI’s monetary policy announcements on government bond yields: evidence from the pandemic”, Indian Economic Review, Vol. 58 No. Suppl 2, pp. 261-291, doi: 10.1007/s41775-023-00171-2. Mukherjee, D. (2007), “Comparative analysis of Indian stock market with international Markets,Great Lakes Herald”, Great Lakes Institute of Management, Vol. 1. Ni, H. (2023), “The global effect of fed rate hikes: stocks, bonds, and foreign exchange markets”, Advances in Economics, Management and Political Sciences, Vol. 30 No. 1, pp. 144-151, doi: 10.54254/2754-1169/30/20231461. Ramsharan, N. (2019), “Impact of interest rate on stock market : challenges for investors(2019)”, International Journal of Innovative Science, Engineering and Technology, Vol. 6 No. 4, pp. 2348-7968. Rossi, W. (2013), Chapter 21 Advances in Forecasting under Instability, Handbook of economic forecasting, Vol: 2 No. Part: B, pp. 1203-1324. Sahu, T.N. and Pandey, K.D. (2020), “Money supply and equity price movements during the liberalized period in India”, Global Business Review, Vol. 21 No. 1, pp. 108-123, doi: 10.1177/ 0972150918761084. Shojaie, A. and Fox, E.B. (2022), “Granger causality: A review and recent advances (March 1, 2022)”, Annual Review of Statistics and Its Application, Vol. 9 No. 1, pp. 289-319, available at: http:// dx.doi.org/10.1146/annurev-statistics-040120-010930 AJEB 9,2 282 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025 Thorbecke, W. (2023), “The impact of monetary policy on the U.S. Stock market since the COVID-19 pandemic”, International Journal of Financial Studies, Vol. 11 No. 4, p. 134, doi: 10.3390/ ijfs11040134. Ugurlu, E., Kocaarslan, B. and Ordu, B.M. (2021), “Monetary policy uncertainty, investor sentiment, and the U.S. stock market performance: new evidence from nonlinear cointegration analysis”, International Journal of Finance and Economics, Vol. 26 No. 2, pp. 1724-1738, doi: 10.1002/ ijfe.1874. Further reading Abdul, J. and Hamid Rao, N. (2019), Chapter 8 – time series analysis (stationarity, cointegration, and causality), in € Ozcan, B. and € Ozt€ urk, I., (Eds), Environmental Kuznets Curve (EKC), Academic Press, pp. 85-99, doi: 10.1016/B978-0-12-816797-7.00008-4, available at: https:// www.sciencedirect.com/science/article/pii/B9780128167977000084. Aggarwal, S. and Agarwal, A. (2017), “Interest rates and stock market in Indian context: an analysis”, Vol. 10, pp. 17-22, available at: https://www.researchgate.net/publication/340444077_interest_ rates_and_stock_market_in_indian_context_an_analysis Ahmed, S. (2008), “Aggregate economic variables and stock markets in India”, International Research Journal of Finance and Economics, No. 14, pp. 141-164, available at: https://ssrn.com/abstract= 1693544 Briggs, W.M. (2023), “A partial solution for the replication crisis in economics”, Asian Journal of Economics and Banking, Vol. 7 No. 2, pp. 180-190, doi: 10.1108/AJEB-03-2023-0027. Chaudhary, R., Bakhshi, P. and Gupta, H. (2020), “Volatility in international stock markets: an empirical study during COVID-19”, JRFM, Vol. 13 No. 9, pp. 1-17, September. RePEc:gam: jjrfmx:v:13:y:2020:i:9:p:208-:d:412561, doi: 10.3390/jrfm13090208. Gu, G., Zhu, W. and Wang, C. (2022), “Time-varying influence of interest rates on stock returns: evidence from China”, Economic Research-Ekonomska Istra� zivanja, Vol. 35 No. 1, pp. 2510-2529, doi: 10.1080/1331677X.2021.1966639. Guo, K., Zhou, W.-X., Cheng, S.-W. and Sornette, D. (2011), “The US stock market leads the federal funds rate and treasury bond yields”, PLoS One, Vol. 6 No. 8, e22794, doi: 10.1371/ journal.pone.0022794. Kalia, A. (2024), “Promoter share pledging and dividend payouts in India: does family involvement matters?”, Asian Journal of Economics and Banking, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/AJEB-01-2024-0009. Profillidis, V.A. and Botzoris, G.N. (2019), Modeling of Transport Demand, Elsevier, pp. 271-351, doi: 10.1016/B978-0-12-811513-8.00007-8, available at: https://www.sciencedirect.com/science/ article/pii/B9780128115138000078 Sampene, A.K., Li, C., Cui, H., Pearl, A., Fredrick, O.A., Robert, B. and Ayisi, A.L. (2021), “The effects of interest rate on stock market; empirical evidence from the Ghana stock exchange”, EPRA International Journal of Economic and Business Review, pp. 27-37, doi: 10.36713/ epra6790. Sun, Y. and Wang, X. (2018), “Asymmetric effects of China’s monetary policy on the stock market: evidence from a nonlinear VAR mode”, Asian Economic and Financial Review, Vol. 8 No. 6, pp. 745-761, doi: 10.18488/journal.aefr.2018.86.745.761. Yadav, M.P., Khera, A. and Mishra, N. (2022), “Empirical relationship between macroeconomic variables and stock market: evidence from India”, Management and Labour Studies, Vol. 47 No. 1, pp. 119-129, doi: 10.1177/0258042X211053166. Corresponding author Subhash Karmakar can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Asian Journal of Economics and Banking 283 Downloaded from http://www.emerald.com/ajeb/article-pdf/9/2/261/10087009/ajeb-11-2024-0127en.pdf by ZBW German National Library of Economics user on 16 December 2025