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Artykuły w czasopismach na temat „NIFTY”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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1

Kumar, Pushpender, Noella Nazareth, and Harsh Pratap Singh. "Do Monetary Policy Announcements Affect Stock Market Performance: Evidence from Emerging Economy." Journal of Commerce and Accounting Research 14, no. 4 (2025): 65–76. https://doi.org/10.21863/jcar/2025.14.4.007.

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This study aims to examine the effect of monetary policy announcements on returns in the Indian stock market. An event study methodology is employed to evaluate the influence of such announcements. The research utilises daily time series data from broad market indices like the Nifty 50, Nifty 100, Nifty 200, and Nifty 500 to represent the Indian stock market, alongside sectoral indices including Nifty Auto, Nifty Bank, Nifty Financial Services, Nifty FMCG, Nifty IT, Nifty Media, Nifty Metal, Nifty Pharma, Nifty Private Bank, Nifty PSU Bank, and Nifty Realty. The results reveal that a reduction

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2

Vijay, Dr S., and Dr V. Prabakaran. "A study on Bank and IT nifty influence on Nifty 50." Journal of University of Shanghai for Science and Technology 23, no. 12 (2021): 316–22. http://dx.doi.org/10.51201/jusst/21/121030.

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NIFTY 50 is one of the benchmark indices which is been used in India. The index is built upon by the companies which are represented from various sectors. Each sector is given separate weightage depending upon the representations from each sector. This paper focused on to identify Pairwise Granger Causality between NIFTY 50 Index, NIFTY Bank Index and NIFTY IT Index. The researcher also focused on to identify the influence of Bank NIFTY and IT NIFTY and NIFTY 50 Index. The research was carried out with a total of 532 observations (closing value of each index) of spanned across January 2018 – F

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3

Pranchana, R., S. Sudhamathi, and S. Benneet. "Evaluating the Impact of Sectoral Indices on Stock Market Performance in the National Stock Exchange." Indian Journal of Information Sources and Services 15, no. 1 (2025): 238–43. https://doi.org/10.51983/ijiss-2025.ijiss.15.1.30.

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Economic growth can be measured by the stock market index industry survey, which measures the key indicators of a country's economic development. Furthermore, analyzing various indicators assists governments and investors in using them as a reference. This paper aims to explore the capital market efficiency of the NSE sector indexes by analyzing daily stock price returns. The study seeks to evaluate the effectiveness of the weak form of the selected indicators listed in the NSE. The paper will assess market efficiency by utilizing series and autocorrelation tests and testing the selected NSE i

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4

Amuthan, R. "Co relating NIFTY 50 Index Trend’s impact on NSE’s Sector based Indices Growth Momentum in Post COVID-19 led Indian Economy with Special reference to NIFTY Bank, NIFTY Consumer Durables, NIFTY IT and NIFTY Pharma Indices using Arithmetic Modelling." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 2184–89. http://dx.doi.org/10.17762/turcomat.v12i6.4824.

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The NIFTY 50 is the flagship index on the National Stock Exchange of India Ltd. (NSE). The Index tracks the behavior of a portfolio of blue chip companies, the largest and most liquid Indian securities. It includes 50 of the approximately 1600 companies traded (listed & traded and not listed but permitted to trade) on NSE, captures approximately 65% of its float-adjusted market capitalization and is a true reflection of the Indian stock market. This study probed in to the correlation between NIFTY 50 and NIFTY Bank, NIFTY 50 and NIFTY Consumer Durables, NIFTY 50 and NIFTY IT and NIFTY 50 a

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5

Narayan, Parab, and Y. V. Reddy. "Exploring the Causal Relationship Between Stock Returns, Volume, and Turnover across Sectoral Indices in Indian Stock Market." Metamorphosis: A Journal of Management Research 16, no. 2 (2017): 122–40. http://dx.doi.org/10.1177/0972622517730140.

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The traditional saying “Market Discounts Everything” is applicable to stock returns, trading volume, and turnover as well. The present study is an analytical attempt to examine the causal relationship between stock returns, trading volume, and turnover across 10 sectoral indices of National Stock Exchange (NSE) for the period 2006–2016. To critically examine this relation, the study uses various statistical techniques such as descriptive statistics, correlation analysis, regression analysis, and econometric tests such as Granger causality test and augmented Dickey–Fuller test. The required ana

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6

Dharani, M. "Seasonal Anomalies between S&P CNX Nifty Shariah Index and S&P CNX Nifty Index in India." Journal of Social and Development Sciences 1, no. 3 (2011): 101–8. http://dx.doi.org/10.22610/jsds.v1i3.633.

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The present study compares the risk and return of the Nifty Shariah index and Nifty index at days, months and quarters wise during the period 2nd January 2007 to 31st December 2010. The raw returns of the both indices are calculated as today price minus yesterday price divided by yesterday price. The t- test has been used to test the mean returns difference between both indices. The average Monday return of the Nifty Shariah index is compared with average return of the Nifty index by using two sample t-test. Like that, the average returns of the remaining of the days of Nifty Shariah index are

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7

Ashri, Dhananjay, Bibhu Prasad Sahoo, Ankita Gulati, and Irfan UL Haq. "Repercussions of COVID-19 on the Indian stock market." Linguistics and Culture Review 5, S1 (2021): 1495–509. http://dx.doi.org/10.21744/lingcure.v5ns1.1792.

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The present paper determines the repercussions of the coronavirus on the Indian financial markets by taking the eight sectoral indices into account. By taking the sectoral indices into account, the study deduces the impact of virus outbreak on the various sectoral indices of the Indian stock market. Employing Welch's t-test and Non-parametric Mann-Whitney U test, we empirically analysed the daily returns of eight sectoral indices: Nifty Auto, Nifty FMCG, Nifty IT, Nifty Media, Nifty Metal, Nifty Oil and Gas, Nifty Pharma, and Nifty Bank. The results unveiled that pandemic had a negative impact

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8

RJ Prashanth and Dr. B Ramya. "THE COMPARATIVE ANALYSIS OF SECTORIAL INDICES WITH NSE NIFTY 50." Juni Khyat 15, no. 02 (2025): 104–8. https://doi.org/10.36893/jk.2025.v15i2.031.

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This study explores the correlation between the NIFTY 50 index and sectoral indices like NIFTY Bank, NIFTY Auto, NIFTY Energy, and NIFTY IT on the NSE. Using statistical methods such as Pearson’s correlation, regression analysis, and ANOVA, the findings reveal a strong correlation between the NIFTY 50 and sectors like banking, auto, and energy, with the IT sector showing a moderate relationship. The results provide insights for investors on sectoral diversification and optimizing portfolio performance.

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9

Stevens, Kerrie. "Nifty Newsletters." ANZTLA EJournal, no. 15 (September 15, 2017): 94–101. http://dx.doi.org/10.31046/anztla.vi15.460.

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10

Parlante, Nick, David Matuszek, Jeff Lehman, David Reed, John K. Estell, and Donald Chinn. "Nifty assignments." ACM SIGCSE Bulletin 36, no. 1 (2004): 46–47. http://dx.doi.org/10.1145/1028174.971318.

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11

Parlante, Nick. "Nifty reflections." ACM SIGCSE Bulletin 39, no. 2 (2007): 25–26. http://dx.doi.org/10.1145/1272848.1272876.

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12

Kim, Tae-Il. "Nifty fifty." Journal of Periodontal & Implant Science 40, no. 3 (2010): 103. http://dx.doi.org/10.5051/jpis.2010.40.3.103.

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13

Parlante, Nick. "Nifty assignments." ACM SIGCSE Bulletin 40, no. 1 (2008): 112–13. http://dx.doi.org/10.1145/1352322.1352173.

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14

Parlante, Nick, Thomas P. Murtagh, Mehran Sahami, et al. "Nifty assignments." ACM SIGCSE Bulletin 41, no. 1 (2009): 483–84. http://dx.doi.org/10.1145/1539024.1509031.

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15

Fuhrman, Jed A., and Douglas G. Capone. "Nifty nanoplankton." Nature 412, no. 6847 (2001): 593–94. http://dx.doi.org/10.1038/35088159.

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16

Seppa, Nathan. "Nifty Spittle." Science News 163, no. 3 (2003): 37. http://dx.doi.org/10.2307/4014174.

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17

Parlante, Nick. "Nifty Assignments." ACM SIGCSE Bulletin 41, no. 2 (2009): 83–84. http://dx.doi.org/10.1145/1595453.1595479.

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18

Parlante, Nick, Jeffrey Popyack, Stuart Reges, et al. "Nifty assignments." ACM SIGCSE Bulletin 35, no. 1 (2003): 353–54. http://dx.doi.org/10.1145/792548.611914.

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19

Parlante, Nick, David Levine, Steven Andrianoff, et al. "Nifty assignment." ACM SIGCSE Bulletin 37, no. 1 (2005): 371–72. http://dx.doi.org/10.1145/1047124.1047356.

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20

Parlante, Nick, John K. Estell, David Reed, David Levine, Dan Garcia, and Julie Zelenski. "Nifty assignments." ACM SIGCSE Bulletin 34, no. 1 (2002): 319–20. http://dx.doi.org/10.1145/563517.563466.

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21

Parlante, Nick. "Nifty assignments." ACM SIGCSE Bulletin 33, no. 4 (2001): 25–27. http://dx.doi.org/10.1145/572139.572162.

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22

Parlante, Nick, John Cigas, Angela B. Shiflet, et al. "Nifty assignments." ACM SIGCSE Bulletin 39, no. 1 (2007): 497–98. http://dx.doi.org/10.1145/1227504.1227479.

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23

Parlante, Nick, Steven A. Wolfman, Lester I. McCann, et al. "Nifty assignments." ACM SIGCSE Bulletin 38, no. 1 (2006): 562–63. http://dx.doi.org/10.1145/1124706.1121516.

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24

P.R, Roshni, and E. Sulaiman. "PERFORMANCE OF NIFTY 50 EXCHANGE TRADED FUNDS." International Journal of Advanced Research 9, no. 02 (2021): 77–83. http://dx.doi.org/10.21474/ijar01/12420.

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The study evaluated the performance of selected Nifty 50 ETFs tracking Nifty 50 Index listed in National Stock Exchange in India during a period of six years starting from 1st April, 2014 to 31st March, 2020. The performance of ETFs is measured using Average Daily Returns, CAGR, HPR, Standard Deviation, Tracking Error, R squared and Beta. It is found that there is difference in the risk-return pattern of Nifty 50 ETFs and its index Nifty 50. Aditya Birla Nifty ETF is the performing fund among the selected ETFs.

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25

Siddiqui, Saif, and Safika Praveen Sheikh. "Modelling the Return of Shariah with Underlying Indices of National Stock Exchange of India: A Case of 3SLS and GMM Estimation." Journal of Emerging Economies and Islamic Research 4, no. 2 (2016): 6. http://dx.doi.org/10.24191/jeeir.v4i2.9082.

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Shariah indices can be used to construct socially reliable investment products that are attractive for those, who do not wish to invest in undesired business. National Stock Exchange of India introduced Nifty 50 Shariah and Nifty 500 Shariah indices to provide alternative i The study is an attempt to reveal the relationship between Nifty 50 Shariah and Nifty 500 Shariah with their underlying indices, Nifty 50 and Nifty 500.For this purpose a period of 01/01/2007 to 31/12/2015 is taken. Based on various objectives, techniques like Descriptive statistics, Correlation, Co 3SLS and GMM estimation

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26

Vikram, Debasis Mohanty, and Archa Agrawal. "An Empirical Study on the Impact of FII and DII on Volatility, Leverage and Long-Term Returns of the Indian Stock Index." Asian Journal of Economics, Business and Accounting 25, no. 4 (2025): 131–37. https://doi.org/10.9734/ajeba/2025/v25i41739.

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Estimating volatility is the key factor to be analyzed in taking the financial decisions. Financial strategies are framed after due investigation of financial market volatility. This study examines the impact of foreign institutional investment (FII) & Domestic Institutional investment (DII) in Indian stock market and analyses the volatility of National Stock Exchange (NSE) categorical indices for the period of 10 years from 20th February 2014 to 20th February 2024. The study is conducted using the logarithmic return of series of Nifty 50, Nifty Midcap 50 & Nifty Small Cap 50. GARCH (1

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27

Garrison, Lehman H., Dan Foreman-Mackey, Yu-hsuan Shih, and Alex Barnett. "nifty-ls: Fast and Accurate Lomb–Scargle Periodograms Using a Non-uniform FFT." Research Notes of the AAS 8, no. 10 (2024): 250. http://dx.doi.org/10.3847/2515-5172/ad82cd.

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Abstract We present nifty-ls, a software package for fast and accurate evaluation of the Lomb–Scargle periodogram. nifty-ls leverages the fact that Lomb–Scargle can be computed using a non-uniform fast Fourier transform (NUFFT), which we evaluate with the Flatiron Institute NUFFT package (finufft). This approach achieves a many-fold speedup over the Press & Rybicki method as implemented in Astropy and is simultaneously many orders of magnitude more accurate. nifty-ls also supports fast evaluation on GPUs via CUDA and integrates with the Astropy Lomb–Scargle interface. nifty-ls is publicly

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28

Singh, Gurmeet. "Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures." Jindal Journal of Business Research 6, no. 2 (2017): 108–31. http://dx.doi.org/10.1177/2278682117715358.

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This study attempts to study and suggest an optimal hedge ratio to Indian investors and traders by examining the three main indices of National Stock Exchange of India (NSE), namely, NIFTY, Bank NIFTY, and IT NIFTY, over the sample period from January 2011 to December 2015. The present study estimated the hedge ratio through six econometric models, namely, OLS, GARCH, EGARCH, TARCH, VAR, and VECM, in the minimum variance hedge ratio framework as suggested by Ederington (1979). The findings of the present study confirm the theoretical properties of Indian cash and futures market and suggest tha

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29

Babu, Manivannan, A. Antony Lourdesraj, C. Hariharan, et al. "Dynamics of Volatility Spillover between Energy and Environmental, Social and Sustainable Indices." International Journal of Energy Economics and Policy 12, no. 6 (2022): 50–55. http://dx.doi.org/10.32479/ijeep.13482.

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The purpose of this research was to examine the dynamics of volatility spillover between energy and environmental, social, and sustainable indices. COVID19 prompted the research to select April 2019 to March 2022 as a sample period, and the respective data (Daily Prices) of the Nifty Energy and Nifty ESG indices were obtained from the National Stock Exchange of India Limited. The outcomes of the study confirmed that the daily returns of Nifty Energy and Nifty 100 ESG indices were not normally distributed and reached stationarity at level difference. Further, the study employed GARCH Models suc

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30

Siddiqui, Saif, and Safika Praveen Sheikh. "Modelling the Return of Shariah with Underlying Indices of National Stock Exchange of India: A Case of 3SLS and GMM Estimation." Journal of Emerging Economies and Islamic Research 4, no. 2 (2016): 6–20. https://doi.org/10.24191/jeeir.v4i2.6314.

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Shariah indices can be used to construct socially reliable investment products that are attractive for those, who do not wish to invest in undesired business. National Stock Exchange of India introduced Nifty 50 Shariah and Nifty 500 Shariah indices to provide alternative indices for Shariah compliant companies. The study is an attempt to reveal the relationship between Nifty 50 Shariah and Nifty 500 Shariah with their underlying indices, Nifty 50 and Nifty 500.For this purpose a period of 01/01/2007 to 31/12/2015 is taken. Based on various objectives, techniques like Descriptive statistics, C

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31

D., Bhuvaneshwari. "Impact of Covid-19 on the Financial Sector Indices." International Research Journal of Business Studies 14, no. 2 (2021): 137–45. http://dx.doi.org/10.21632/irjbs.14.2.137-145.

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This study is an attempt to assess the impact of Covid-19 and the lockdown pronounced thereof on the Nifty sectoral indices with specific reference to the financial sector indices owing to their significance in the economy. The OLS regression, Granger Causality and Impulse Response Function were estimated to measure the changes in the future responses of Nifty 50 to the changes in the select sectoral indices, namely, Nifty Bank, Nifty Financial Services and Nifty Private Banks and Nifty PSU Banks for the period consisting two sub-periods, i.e., the first sub-period from April 2019 to March 202

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32

Powell, John Y. "The Nifty Nine." Residential Treatment For Children & Youth 9, no. 2 (1992): 85–95. http://dx.doi.org/10.1300/j007v09n02_08.

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33

Mervis, J. "A NIFty Idea." Science 320, no. 5875 (2008): 437d. http://dx.doi.org/10.1126/science.320.5875.437d.

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34

JACOBY, MITCH, and LAUREN K. WOLF. "NIFTY AT FIFTY." Chemical & Engineering News 88, no. 10 (2010): 13–17. http://dx.doi.org/10.1021/cen-v088n010.p013.

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35

Parlante, Nick, Mike Clancy, Stuart Reges, Julie Zelenski, and Owen Astrachan. "Nifty assignments panel." ACM SIGCSE Bulletin 33, no. 1 (2001): 412–13. http://dx.doi.org/10.1145/366413.364797.

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36

Parlante, Nick, Owen Astrachan, Mike Clancy, Richard E. Pattis, Julie Zelenski, and Stuart Reges. "Nifty assignments panel." ACM SIGCSE Bulletin 31, no. 1 (1999): 354–55. http://dx.doi.org/10.1145/384266.299809.

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37

Pushkar Dilip Parulekar. "Systematic Investment Plan vs. Lumpsum Investment: A Comparative study across Time and Indexes." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 362–82. https://doi.org/10.52783/cana.v32.3681.

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Introduction: SIP is based on the logic of rupee cost averaging wherein regular periodic investments are made (generally monthly) as LI which means one time investment There is always a debate between active and passive investing. Even though some active investors might outperform passive investors, there will be balancing underperformers as well. Considering transaction cost and risk adjusted returns passive investors tend to outperform the active investors over the longer time horizon. Objectives: This paper compares success of two popular methods of passive investing that could be used by r

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38

Rajesh, K. Sadhwani. "A Study on Nifty 50 during COVID 19 Pandemic." Empirical Economics Letters 22, no. 8 (2023): 83–91. https://doi.org/10.5281/zenodo.10030843.

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<strong>Abstract: </strong>This study aims to understand the performance of Nifty 50 during COVID-19 pandemic. The paper attempts to predict the post COVID-19 second wave value of the Nifty 50 and check the efficiency of ARIMA model in short duration. To study and forecast the performance of Nifty 50 daily closing prices of Nifty 50 index is considered. The historical data are collected from NSE's website. Analysis shows that index has given immense opportunities during the COVID 19 pandemic to enter in equity market. The ARIMA model indicates the range bound movement with 95 per cent of

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39

Dharani, M. "Equanimity of Risk and Return Relationship between Shariah Index and General Index in India." Journal of Economics and Behavioral Studies 2, no. 5 (2011): 213–22. http://dx.doi.org/10.22610/jebs.v2i5.239.

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The present study empirically examines the risk and return of the Nifty Shariah index and Nifty index during the period 2nd January 2007 to 31st December 2010. The sample period is further divided into bull market period and bear market period based on the movement of the both indices during the study period. The objective of the study is to analyse the performance of the Islamic index and common index and to test whether any significant difference between both indices in India. Based on the previous studies, the present paper employs Risk adjusted measurement such as Sharpe index, Treynor Ind

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40

Kotha, Kiran Kumar, and Shreya Bose. "Dynamic Linkages between Singapore and NSE listed NIFTY Futures and NIFTY Spot Markets." Journal of Prediction Markets 10, no. 2 (2017): 1–13. http://dx.doi.org/10.5750/jpm.v10i2.1253.

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This study examines the dynamic linkages of Nifty stock index and Nifty index futures contract traded on the home market, National Stock Exchange (NSE) and on the off-shore market, Singapore Stock Exchange (SGX). The study uses daily closing prices of the Nifty index and the Nifty futures contract traded on both the exchanges for the period July 15, 2010 to July 15, 2016. The study finds a causality running from the returns of the spot market to the returns from the Nifty futures market in both the exchanges, NSE and SGX, with the help of Vector Error Correction model and Granger causality tes

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41

N., Dileep, and G. Kotreshwar. "INTER RELATIONSHIP BETWEEN RAINFALL INDEX AND NIFTY INDEX: AN EMPIRICAL STUDY." JOURNAL OF APPLIED FINANCIAL ECONOMETRICS 3, no. 1 (2022): 53–63. http://dx.doi.org/10.47509/jafe.2022.v03i01.03.

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The proposed study is an attempt to determine whether a relationship exists between rainfall index and NSE Nifty index. The study used the monthly mean rainfall data and monthly closing price of Nifty index. The study applied Augmented Dickey-Fuller (ADF) test, correlation analysis, the GARCH (1,1) model, and the Granger Causality test to analyse the interrelationship. The results of correlation matrix show that there is no interrelationship between the two variables. The GARCH (1,1) model found that the NSE Nifty index is not affected by the rainfall index and Granger Causality test displays

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42

N, Dileep, and G. Kotreshwar. "Interrelationship between Rainfall Index and Nifty Index: An Empirical Study." Gyan Management Journal 16, no. 2 (2022): 47–54. http://dx.doi.org/10.48165/gmj.2022.16.2.7.

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The proposed study is an attempt to determine whether a relationship exists between rainfall index and NSE Nifty index. The study used the monthly mean rainfall data and monthly closing price of Nifty index. The study applied Augmented Dickey-Fuller (ADF) test, correlation analysis, the GARCH (1,1) model, and the Granger Causality test to analyse the interrelationship. The results of correlation matrix show that there is no interrelationship between the two variables. The GARCH (1,1) model found that the NSE Nifty index is not affected by the rainfall index and Granger Causality test displays

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P. Sakthivel, S. Rajaswaminathan, R. Renuka, and N. R.Vembu. "Dynamic Relationship between Crude Oil and Stock Prices in India: Before And After the Subprime Financial Crisis 2008." GIS Business 14, no. 6 (2019): 96–104. http://dx.doi.org/10.26643/gis.v14i6.11683.

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This paper empirically discovered the inter-linkages between stock and crude oil prices before and after the subprime financial crisis 2008 by using Johansan co-integration and Granger causality techniques to explore both long and short- run relationships. The whole data set of Nifty index, Nifty energy index, BSE Sensex, BSE energy index and oil prices are divided into two periods; before crisis (from February 15, 2005 to December31, 2007) and after crisis (from January 1, 2008 to December 31, 2018) are collected and analyzed. The results discovered that there is one-way causal relationship f

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44

K. Riyazahmed, K. "VOLATILITY SPILLOVER AND PANDEMIC – ANALYSIS OF SELECTED SECTORAL INDICES IN INDIA." Economic Thought journal 67, no. 6 (2022): 655–70. http://dx.doi.org/10.56497/etj2267602.

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The COVID-19 pandemic has impacted economies worldwide, and it has been reflected in their stock markets, as well. The effect was evident in the Indian stock markets, yet the nature and level of this impact are not very clear. This paper examines the short- and long-term spillover in the volatility between coronavirus cases on the broader market index, Nifty 50, and the indices of selected sectors: information technology, healthcare, and pharmaceuticals. Data for the period from January 2020 to July 2022 has been analyzed. The Dynamic Conditional Correlation GARCH model was used for analyzing

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Gangwani, Mayank, and Dhun Sehrawat. "Covid-19-A Baleful Aftermath for the Stocks of Indian Pharmaceutical Companies." International Journal of Science, Engineering and Management 9, no. 9 (2022): 21–31. http://dx.doi.org/10.36647/ijsem/09.09.a004.

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Covid-19 catastrophe has not spared any market across the world due to widespread disruptions in its supply chain operations. In today's world, however, stock markets serve as a catalyst for a country's economic and financial development. But, with the development of Covid-19 infection and widespread lockdown in the majority of countries, its stock market has plummeted even further into the depths. Therefore, to determine whether the Covid-19 outbreak has impacted the expected return of Nifty Pharma and stock return of 3 leading pharmaceutical companies Cipla, Dr. Reddy's, and Sun Pharma in th

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Garg, Deeksha. "Does the Nature of Index and Liquidity Influence the Mispricing in Future Contracts in India?" Applied Finance Letters 9, SI (2020): 15–22. http://dx.doi.org/10.24135/afl.v9i2.244.

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In this study, we investigate the variations in the mispricing of futures in Nifty (benchmark index), Bank Nifty and Nifty IT. Using a regression model on 1230 observations for the period of 1 January 2014 to 31 December 2018, we find no significant mispricing exists in the last week to the expiry of the contract in all three indices. This finding supports the existing literature that as the contract moves towards the maturity date, its value converges the market value. However, the main highlight of the paper is to reveal the difference in the life of mispricing in different indices. This dif

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Nidhi, Agrawal, Srinivasan P., and Shroff Sumita. "Revisiting the Cointegration and Casual Relationship between Different Financial Markets." Empirical Economics Letters 22, no. 12 (2023): 97–108. https://doi.org/10.5281/zenodo.10460559.

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<strong>Abstract: </strong>This study aims to examine the causal link among USD/INR exchange rates, domestic gold prices, crude oil prices, and NIFTY 50 index taking a long period of 13 years from September 2010 to September 2023. We use Augmented Dickey–Fuller (ADF) unit root test, Autoregressive Distributed Lag Model (ARDL) and Granger causality test for the analysis. ARDL results indicate the existence of a long-run relationship among USD/INR, NIFTY 50 and crude prices but absence in gold prices. Granger causality test result reveals a bidirectional causality between the NIFTY index a

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Ainapur, Jyoti, Maheshkumar Maharudrappa, Harshavardhan M, et al. "PERFORMANCE MATRIX: COMPARATIVE INSIGHTS INTO NIFTY'S BANKING, IT, FMCG, PHARMA, ENERGY, AND INFRASTRUCTURE SECTORS." International Journal of Research in Commerce and Management Studies 06, no. 05 (2024): 64–85. http://dx.doi.org/10.38193/ijrcms.2024.6504.

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This study presents a comprehensive performance matrix of six key Nifty sectors - Banking, IT, FMCG, Pharma, Energy, and Infrastructure - over the decade spanning 2014-2024. Employing a mixed-methods approach, this research analyzes sector-specific trends, volatility patterns, and yearly returns to provide actionable insights for investors. The findings reveal significant performance differentials, with Nifty IT emerging as the top-performing sector, while Nifty FMCG demonstrates resilience and Nifty Bank exhibits volatility. Statistical analysis and hypothesis testing validate these trends. T

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Dr. Rajani. "Impact of Automobile Companies Stock Returns on Indian Stock Market Indices with Special Reference to Nifty Fifty Index." International Journal of Management and Humanities 10, no. 5 (2024): 31–37. http://dx.doi.org/10.35940/ijmh.e1676.10050124.

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The Nifty Fifty Index has a free-float market capitalization-weighted index.it represents the weighted average of the top 50 Indian companies listed on the National Stock Exchange ,and it is one of the main stock indices being used in India, besides the BSE Sensex of the Bombay Stock Exchange. The Nifty 50 index represents 62% of the free float market capitalization of the stocks listed in the NSE (NATIONAL STOCK EXCHANGE) as on September 30,2022.sectors ‘s stocks are performing more consistently and provide high return with high risk in (NSE)NATIONAL STOCK EXCHANGE ,these stocks are emerging

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Dr., Rajani. "Impact of Automobile Companies Stock Returns on Indian Stock Market Indices with Special Reference to Nifty Fifty Index." International Journal of Management and Humanities (IJMH) 10, no. 5 (2024): 31–37. https://doi.org/10.35940/ijmh.E1676.10050124.

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<strong>Abstract:</strong> The Nifty Fifty Index has a free-float market capitalization-weighted index.it represents the weighted average of the top 50 Indian companies listed on the National Stock Exchange ,and it is one of the main stock indices being used in India, besides the BSE Sensex of the Bombay Stock Exchange. The Nifty 50 index represents 62% of the free float market capitalization of the stocks listed in the NSE (NATIONAL STOCK EXCHANGE) as on September 30,2022.sectors ‘s stocks are performing more consistently and provide high return with high risk in (NSE)NATIONAL STOCK EXC

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