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Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Sectoral Indices.

Статті в журналах з теми "Sectoral Indices"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Robiyanto, Robiyanto. "Penggunaan Metode Orthogonal GARCH untuk Meramalkan Matriks Kovarians Return Indeks Harga Saham Sektoral Di Bursa Efek Indonesia." Jurnal Ekonomi Kuantitatif Terapan 12, no. 2 (2019): 30. http://dx.doi.org/10.24843/jekt.2019.v12.i02.p05.

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ABSTRACT  This study conducted a risk communality assessment on sectoral stock price indices in Indonesia Stock Exchange by using Orthogonal Generalized Autoregressive Conditional Heteroscedasticity (Orthogonal GARCH) method. Data used in this research is daily closing of sectoral stock price indices at Indonesia Stock Exchange which consisting of 10 sectoral price indices. Research period are during January 4, 2011 until July 17, 2017. Of 10 sectoral stock price indices which studied apparently there are two principal component influencing its conditional variance. The result of t

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2

Sucuahi, William T. "Predicting Long-Run and Short-Run Movement of Sectoral Index: Evidence From Philippine Stock Market." International Journal of Financial Research 14, no. 2 (2023): 18. http://dx.doi.org/10.5430/ijfr.v14n2p18.

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The financial markets provide a viable avenue for investors who wants to invest their idle resources. Investors need accurate information to minimize investment risk and make the right investment decision. This study attempted to test the predictability of the Philippine Stock Exchange (PSE) sectoral indices. The data used in this study are the daily closing price of the six sectoral indices from January 2010 to December 2019. Augmented Dickey-Fuller (ADF) for stationarity test and Johansen Cointegration and Granger Causality analysis were used to test the long-run and short-run relationship a

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3

Rudzkis, Rimantas, and Roma Valkavičienė. "ECONOMETRIC MODELS OF THE IMPACT OF MACROECONOMIC PROCESSES ON THE STOCK MARKET IN THE BALTIC COUNTRIES." Technological and Economic Development of Economy 20, no. 4 (2014): 783–800. http://dx.doi.org/10.3846/20294913.2014.949901.

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The article examines the dependencies of individual sectoral stock price indices of OMX Baltic security market on macroeconomic indicators, using econometric methods. Regression models are constructed using quarterly time series of 2000–2011 years while the methodology is backed with the findings of Lithuanian and foreign scientists from an extensive overview of specific literature. Regression equations, obtained in the paper, allows us to identify the key macroeconomic and global indicators that statistically significantly affect the Baltic securities market and to quantify their impact on th

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4

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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5

El Msiyah, Cherif, Jaouad Madkour, Younes Berouaga, Ayoub Kyoud, and Ali Ait Lahcen. "Moroccan Stock Exchange market topology in crisis and non-crisis periods." Investment Management and Financial Innovations 19, no. 4 (2022): 274–84. http://dx.doi.org/10.21511/imfi.19(4).2022.22.

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This paper seeks to investigate the dynamics within the Moroccan Stock Exchange (MSE) market topology in crisis and non-crisis periods using daily historical log returns of sectoral indices covering the period from January 4, 1993 to September 9, 2021. The study applies the Agglomerative Hierarchical Clustering (AHC) implemented on the Dynamic Time Warping (DTW) distance matrix over ten sub-periods covering numerous crises, from Subprime mortgage crisis to European debt crisis and finally COVID-19 crisis. The obtained clustering results are gathered into a network to display the cumulated inte

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6

Olaniyan Sunday Michael and Abiola Bankole. "Modelling the Relationship between Sectoral Indices of the Stock Market in Nigeria (All Share Index Vs. Other Index)." UMYU Journal of Accounting and Finance Research 1, no. 1 (2023): 100–112. http://dx.doi.org/10.61143/umyu-jafr.1(1)2021.006.

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This study investigated the relationship between the returns of the sectoral indices using correlations analysis and beta analysis on weekly index values of sectoral indices with a base value of 157 points of specific sectors at the Nigerian Stock Exchange (NSE) from 04 October 2013 until 30 September 2016. The result shows that returns across various sectors tend to be correlated which indicated that risk diversification would be difficult. All Share Index returns have a positive relationship with the vast majority of the sectoral indices indicating that many indexes performance is alongside

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7

T. A., Krishna, and Suresha B. "Intensified geopolitical conflicts and herding behavior: An evidence from selected Nifty sectoral indices during India-China tensions in 2020." Investment Management and Financial Innovations 19, no. 1 (2022): 300–312. http://dx.doi.org/10.21511/imfi.19(1).2022.23.

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The recent India-China geopolitical conflicts have presented enormous uncertainty to the investors in various sectoral indices of the Indian stock market. This empirical study aims to examine the impact of intensified India-China geopolitical conflicts 2020 on investors’ herding behavior in the National Stock Exchange sectoral indices. The high-frequency data of three major NIFTY sectoral indices (Auto, Energy, and Pharma) are used in an intensified geopolitical event window to spot precisely the traces of the investors’ herding behavior. Furthermore, multifractal detrended fluctuation analysi

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8

Matha, Rajeev, Geetha E., Satish Kumar, and Raghavendra. "Dynamic relationship between equity, bond, commodity, forex and foreign institutional investments: Evidence from India." Investment Management and Financial Innovations 19, no. 4 (2022): 65–82. http://dx.doi.org/10.21511/imfi.19(4).2022.06.

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The interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign excha

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9

Bernardino, Wilton, Leonardo Brito, Raydonal Ospina, and Silvio Melo. "A GARCH-VaR Investigation on the Brazilian Sectoral Stock Indices." Brazilian Review of Finance 16, no. 4 (2019): 573. http://dx.doi.org/10.12660/rbfin.v16n4.2018.74676.

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In this paper, we have explored operational risk in Brazil by considering different sectoral indices of the Brazilian economy and the GACH Value-at-Risk (GARCH-VaR) estimation approach. We have carried a statistical evaluation of the eight Brazilian sectoral stock indices during different time ranges so that VaR methodologies could be chosen according to the data. We have analyzed the sectoral Brazilian indices during a common time range where we have realized VaR backtests using recent data. The results of the study reveals that VaR may be an effective tool on minimizing risk exposure and pot

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10

Khurshid, Ali, Ahmad Malik Irshad, Ashraf Chisti Khalid, and Showkat Numaira. "Inflation and Stock Market Returns: An Empirical Study of Sectoral Indices with Special Reference to India." Economics and Business Quarterly Reviews 6, no. 1 (2023): 148–54. https://doi.org/10.31014/aior.1992.06.01.493.

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In this study an attempt has been undertaken so as to establish the relationship between sectoral indices returns and inflation numbers. In order to achieve the objective of the study, all the sectoral indices have been taken in the study except two (02) indices because they were introduced in the National Stock Exchange in the recent past and, therefore, their data is not available for the whole reference period of the study. The indices that are in the study are, (CNX Auto Index, CNX Bank Index, CNX Energy Index, CNX Finance Index, CNX FMCG Index, CNX IT Index, CNX Metal Index, CNX MNC Index

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11

S, Ananda Anggara, and Matrodji H. Mustafa. "Does Herding Behavior Exist in the IDX Sectoral Indices." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 642–48. http://dx.doi.org/10.38124/ijisrt20aug253.

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This study aims to detect herding behavior based on cross-sectional dispersion in certain market conditions using CSAD method as proposed by Chiang, Li, & Tan (2010). CSAD method allows researchers to evaluate if there is a herding behavior in the capital market. This research uses 9 (nine) sectoral indices listed on the Indonesia Stock Exchange (IDX) in the 2013-2019 period. This study examines the hypothesis that herding behavior occurs in the sectoral indices of the Indonesia stock market in upward market conditions and downward market conditions. The results showed that herding behavio

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12

Herlando, Aldhi, and Sishadiyati Sishadiyati. "Changes in Inflation and Exchange Rates on Investment Decisions in Sectoral Shares on the Indonesia Stock Exchange." Media Trend 17, no. 2 (2022): 600–607. http://dx.doi.org/10.21107/mediatrend.v17i2.14446.

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This research was conducted on the basis of different perceptions regarding the influence of domestic macroeconomics on stock prices. The independent variables used in this study are inflation and exchange rates, while the dependent variable used is the share price of the Indonesia Stock Exchange's Sectoral Index. This study uses monthly data from January 2018 to December 2020. The analysis technique used is multiple regression analysis. There are three findings in this research. First, inflation has a partial effect on most of the sectoral indices except for the Basic Industry Sector Index, t

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13

Moosa, Dr Jabir, Mr Anil Kumar M, and Dr SURAJ E.S. "DOES INFLATION SPECIAL EFFECTSON THE SECTORIAL INDICES IN THE INDIAN STOCK MARKET? AN EMPIRICAL ANALYSIS." International Journal of Engineering Applied Sciences and Technology 7, no. 10 (2023): 100–109. http://dx.doi.org/10.33564/ijeast.2023.v07i10.013.

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In this study, the involvement of rate of inflation on the sectoral indices in the Indian Stock Market is investigated. Stock indices of various sectors and inflation rate are taken from secondary sources. Correlation is used for data analysis with the help of SPSS to verify whether the rate of inflation has any effect on stock prices. The results of the research shows that the rate of inflation has no direct effect on sectoral stocks, as there are other factors which cause variation in the price of sectoral stocks.

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14

Bouhlal, Fadwa, and Moulay Brahim Sedra. "The effect of the COVID-19 epidemic on Moroccan sectoral indices: The entropy approach." Investment Management and Financial Innovations 19, no. 4 (2022): 232–43. http://dx.doi.org/10.21511/imfi.19(4).2022.19.

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The current study investigates the impact of the Coronavirus 2019 (COVID-19) pandemic on the volatility of Moroccan stock market sectoral indices. Shannon entropy with multiple estimators and Rényi entropy for different scales were calculated from February 1, 2019 to May 1, 2022, to measure volatility in the Banking, Oil and Gas, Construction and Building Materials, Beverage, Food Producers and Processors, Distributors, and Mining sector’s indices. In this regard, this study uses three periods to quantify the uncertainty in Moroccan sectoral indices before, during, and after the first year of

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15

K, Ramya, and Bhuvaneshwari D. "Dynamic Interaction Between Nifty 50 and Nifty Sectoral Indices: An Empirical Study on Indian Stock Indices." NMIMS Management Review 29, no. 02 (2021): 17–24. http://dx.doi.org/10.53908/nmmr.290202.

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This study aims to determine the cointegrating and causal relationship between Nifty 50 and Nifty sectoral indices. Historical index data of the select indices were collected from the National Stock Exchange (NSE) database for the period Jan 2014 - Dec 2018. Appropriate Econometric tools - Augmented Dickey-Fuller (ADF) test, Phillips and Perron (PP) test, regression model, Granger causality test, and Johansen cointegration test were used to analyze the data. The findings of the study imply that the movements of Nifty sectoral index prices could determine the flow of stock index prices, i.e., N

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16

Syed, Abdul Malik, Mahdy Othman, and Mohd Yasir Arafat. "Sectoral interdependence and causal dynamics in Jordanian financial markets: Evidence from benchmark and sectoral indices." International Journal of Innovative Research and Scientific Studies 8, no. 4 (2025): 447–59. https://doi.org/10.53894/ijirss.v8i4.7871.

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The study investigates the financial nexus and causal linkages among the Jordanian benchmark index (AMMAN SE General) and its five major sectoral indices, namely the Jordan Banking Index (AMBX), Industry Index (AMIDX), Mining and Extraction Industries Index (AMMEIX), Service Index (AMSX), and Utility and Energy Index (AMUEX). The daily closing values of all selected six indices are considered for the period spanning January 1, 2013, to June 30, 2024. Various advanced econometric techniques such as the Johansen Cointegration test, Vector Error Correction Model (VECM), and Granger Causality are

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17

Altahtamouni, Farouq, Hajar Masfer, and Shikhah Alyousef. "Dynamic Linkages among Saudi Market Sectors Indices." Economies 10, no. 1 (2022): 16. http://dx.doi.org/10.3390/economies10010016.

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This study aims to test the causal relationship between Saudi stock market index (TASI) and sectoral indices throughout the period from 2016–2020. The study data were extracted through the main index of the Saudi market and the indices of the available data of 19 sectors out of 21 sectors. The unit root test was used along with the Granger causality test, in addition to multiple regression tests in order to analyze the study hypotheses. The study shows that all index series were stationary at the zero level I (0), and the results also show that there were bidirectional and unidirectional causa

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18

Puji Lestari, Novi, and Venus Kusumawardhana. "Effect of COVID-19 on the Performance of Sectoral Indices Listed on the Indonesia Stock Exchange in 2019-2020." JBMP (Jurnal Bisnis, Manajemen dan Perbankan) 8, no. 1 (2022): 1–6. http://dx.doi.org/10.21070/jbmp.v8i1.1622.

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This study aims to: (1) examine and analyze the impact of the pandemic on the development of the Capital Market JCI in Indonesia; (2) analyze the influence of the Covid-19 externality on the dynamics of the development of the Capital Market sectoral index in Indonesia. The research uses a case study method with a quantitative analysis approach using sectoral stock index history data with purposive sampling technique. The research population is 9 sectoral indices listed on the Indonesia Stock Exchange. The research period is November 2019-December 2020 which is analyzed using the Chartnexus ana

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19

Pathak, Harsh Raj, and Satish Kumar. "Do Crude Oil Price Fluctuations Affect the Sectoral Stock Returns: Evidence from India." Journal of Commerce and Accounting Research 13, no. 3 (2024): 31–43. http://dx.doi.org/10.21863/jcar/2024.13.3.004.

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Crude oil is a vital energy source for industrialised and developing countries. Both investors who trade crude oil derivatives and firms that use oil as raw material for production monitor oil supply and demand. Disruption in the flow of oil in the commodity markets, therefore, leads to oil price volatility that affects major economies worldwide. Often, geopolitical and natural conditions could adversely impact the oil price and the financial markets react to such fluctuations in oil prices. This paper analyses the effects of oil price changes on sectorial stock returns in India. Using daily r

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20

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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21

Fasanya, Ismail Olaleke, Oluwatomisin Oyewole, and Taofeek Agbatogun. "Measuring Return and Volatility Spillovers among Sectoral Stocks in Nigeria." Zagreb International Review of Economics and Business 22, no. 2 (2019): 71–93. http://dx.doi.org/10.2478/zireb-2019-0021.

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Abstract This paper examines the return and volatility spillovers of different sectoral stock prices in Nigeria using monthly data from January 2007 to December 2016. We employ the Diebold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the sector stocks market.We show that there is substantial difference between the behaviour of the sectoral stock return and volatility spillover indices over time. We find evidence of interdependence among sector stocks given the spillover indices. While the return spillover index revea

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22

Bhattacharjee, Animesh, and Kuntal Dutta. "The weak-form efficiency of the Indian stock market: Fresh Evidence." CHARTERED ACCOUNTANT 71, no. 8 (2023): 73–79. https://doi.org/10.5281/zenodo.7601965.

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The primary goal of the present research initiative is to determine if the Indian stock market follows a random walk or not. The data on eight nifty sectoral indices’ daily opening, closing, high and low values are taken from a website. The Augmented Dickey–Fuller test and the Phillips–Perron test is used to assessing the stationarity of the selected eight sectoral indices. The Variance ratio test is used to check for auto correlation between the returns and the Runs test is performed to examine if the stock market followed a random walk or not. The unit root tests show that

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23

Chong, Fennee. "Does Adapting an Elimination Strategy Insulate the New Zealand Stock Market Against the COVID-19 Pandemic?" Studies in Business and Economics 17, no. 2 (2022): 80–89. http://dx.doi.org/10.2478/sbe-2022-0026.

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Abstract The objective of this study is to provide insights on whether NZX 50 and sectorial indices of different industries in New Zealand have been impacted differently under the influence of COVID-19 and the measurements that government have taken in responding to the global pandemic. Results indicated that the New Zealand businesses are not immune from this pandemic despite the New Zealand government’s “go hard, go early” measure to eliminate the spread of Coronavirus. Statistical findings show that stock market and sectorial performances between the pre- and post-global pandemic period are

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24

Mittal, Khushi, and Dr Jitender Bhandari. "Boom or bust? A Comparative Analysis of Monetary Policy and Stock Market Performance in India Across Economic Cycles." Indian Journal of Economics and Finance 4, no. 1 (2024): 47–51. http://dx.doi.org/10.54105/ijef.b2577.04010524.

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This study delves into the intricate relationship between monetary policy instruments and various sectors of the Indian stock market from 2011 to 2022. By Analyzing data encompassing significant policy shifts and economic events, including the Reserve Bank of India's (RBI) transition towards inflation targeting and flexible exchange rates, the study aims to uncover the nuanced impacts of monetary policy on sectoral indices. Utilizing a Vector Autoregression (VAR) model, the study examines the dynamic interplay between key monetary policy instruments—Cash Reserve Ratio (CRR), Marginal Standing

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25

Khushi, Mittal. "Boom or bust? A Comparative Analysis of Monetary Policy and Stock Market Performance in India Across Economic Cycles." Indian Journal of Economics and Finance (IJEF) 4, no. 1 (2024): 47–51. https://doi.org/10.54105/ijef.B2577.04010524.

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<strong>Abstract: </strong>This study delves into the intricate relationship between monetary policy instruments and various sectors of the Indian stock market from 2011 to 2022. By Analyzing data encompassing significant policy shifts and economic events, including the Reserve Bank of India's (RBI) transition towards inflation targeting and flexible exchange rates, the study aims to uncover the nuanced impacts of monetary policy on sectoral indices. Utilizing a Vector Autoregression (VAR) model, the study examines the dynamic interplay between key monetary policy instruments—Cash Reserv

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26

Kumar, Dilip. "Long-range dependence in Indian stock market: a study of Indian sectoral indices." International Journal of Emerging Markets 9, no. 4 (2014): 505–19. http://dx.doi.org/10.1108/ijoem-09-2011-0090.

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Purpose – The purpose of this paper is to test the efficient market hypothesis for major Indian sectoral indices by means of long memory approach in both time domain and frequency domain. This paper also tests the accuracy of the detrended fluctuation analysis (DFA) approach and the local Whittle (LW) approach by means of Monte Carlo simulation experiments. Design/methodology/approach – The author applies the DFA approach for the computation of the scaling exponent in the time domain. The robustness of the results is tested by the computation of the scaling exponent in the frequency domain by

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27

R.Rajesh, Ramkumar, and K.Dhanalakshmi. "An Empirical Analysis of Weak Form Ef ciency in Bombay Stock Exchange Sectoral Indices." Shanlax International Journal of Management 6, no. 2 (2019): 59–64. https://doi.org/10.5281/zenodo.2591088.

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Capital Market deals with medium and long term funds. It is an institutional arrangement for borrowing medium and long term funds. The sectoral analysis is typically employed by investors who plan to select better stocks to invest. Hence the study of Sectoral Ef ciency could provide useful input to the Government, Policymakers and Investors to identify the ef cient sectors. This study tested the Market Ef ciency of BSE Bankex as it performed well during the study period.

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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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29

R, Naveen Kumara, and Arockia Baskaran S. "Long Run Relationship between Macroeconomic Indicators and Indian Sectoral Indices." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 1126–32. http://dx.doi.org/10.5373/jardcs/v11/20192681.

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30

Saiti, Buerhan, Yusuf Ma, Ruslan Nagayev, and İbrahim Güran Yumusak. "The diversification benefit of Islamic investment to Chinese conventional equity investors." International Journal of Islamic and Middle Eastern Finance and Management 13, no. 1 (2019): 1–23. http://dx.doi.org/10.1108/imefm-01-2018-0014.

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Purpose The purpose of this paper is to investigate the extent to which Chinese equity investors can benefit from diversifying their portfolio into Shariah-compliant (Islamic) indices. It examines three Islamic stock indices (FTSE Shariah China price index, MSCI China Islamic IMI price index and the DJ Islamic Greater China price index) and ten sectoral indices in Shanghai Stock Exchange as a sample. Design/methodology/approach The multivariate GARCH dynamic conditional correlations (MGARCH-DCC) is deployed to estimate the time-varying linkages of returns of the selected indices, covering appr

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31

Mohamed, Nishad, and K. T. Thomachan. "HOW VOLATILE IS INDIAN STOCK MARKET? A STUDY BASED ON SELECTED SECTORAL INDICES." International Journal of Research -GRANTHAALAYAH 3, no. 12 (2015): 142–49. http://dx.doi.org/10.29121/granthaalayah.v3.i12.2015.2899.

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Анотація:

This paper examines the nature of volatility of selected sectoral stock indices traded in the National Stock Exchange. Using the EGARCH model introduced by Nelson, it has been observed that the selected indices are subject to Autoregressive Conditional Heteroskedasticity (ARCH) effects. There are significant leverage effects in the case of five indices. Volatility seems to be highly persistent in the case of all the indices except one. Moreover, four indices are highly sensitive to market events.

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T., Mohamed Nishad, and Thomachan K.T. "HOW VOLATILE IS INDIAN STOCK MARKET? A STUDY BASED ON SELECTED SECTORAL INDICES." International Journal of Research – Granthaalayah 3, no. 12 (2017): 142–49. https://doi.org/10.5281/zenodo.848963.

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This paper examines the nature of volatility of selected sectoral stock indices traded in the National Stock Exchange. Using the EGARCH model introduced by Nelson, it has been observed that the selected indices are subject to Autoregressive Conditional Heteroskedasticity (ARCH) effects. There are significant leverage effects in the case of five indices. Volatility seems to be highly persistent in the case of all the indices except one. Moreover, four indices are highly sensitive to market events.

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Nisarg A. Joshi. "IMPACT OF COVID-19 ON PERFORMANCE ON INDIAN STOCK INDICES: A STUDY FOR NSE COMPOSITE AND SECTORAL INDICES." Copernican Journal of Finance & Accounting 11, no. 4 (2023): 125–46. http://dx.doi.org/10.12775/cjfa.2022.022.

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Purpose/Objective: The objective of the paper was to scrutinize the impact of COVID-19 on the performance of composite and sectoral indices of National Stock Exchange (NSE) of India. Methodology and Approach: The study included sample daily closing prices from July 2019 to December 2020 for two composite indices and nine sectoral indices of NSE. The sample was divided into three sub-samples to check the impact before, during and after (after the lockdown was lifted) the pandemic on the volatility of returns. The volatility measures were regressed using a dummy variable for COVID-19. Findings:

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B., Shakila, Prakash Pinto, and Iqbal Thonse Hawaldar. "Semi-monthly effect in stock returns: new evidence from Bombay Stock Exchange." Investment Management and Financial Innovations 14, no. 3 (2017): 160–72. http://dx.doi.org/10.21511/imfi.14(3-1).2017.01.

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Semi-monthly effect is a kind of calendar anomalies which is less explored in the financial literature. The main objective of this paper to investigate the presence of semi-monthly effect in selected sectoral indices of Bombay Stock Exchange (BSE). The study uses the daily stock returns of five sectoral indices viz S&amp;amp;P BSE Auto Index, S&amp;amp;P BSE Bankex, S&amp;amp;P BSE Consumer Durables Index, S&amp;amp;P BSE FMCG Index and S&amp;amp;P BSE Health Care Index for the period of 10 years starting from 1st April 2007 to 31st March 2017. The data were analyzed using

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35

Gupta, Vikas, and Sripal Srivastava. "Region Wise Stock Market Performance in India and Covid-19: A Comparative Evaluation Amid Pre and During Covid Era." DME Journal of Management 3, no. 01 (2023): 36–39. http://dx.doi.org/10.53361/dmejm.v3i01.06.

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This paper attempts to investigate the sectoral wise stock market behavior pre andduring the COVID period. To this end, the daily closing prices of eight sectoral indices of Nifty are taken. The pre-COVID period was taken as the FY 2019-20 and during COVIDperiod was taken as the FY 2020-21. In this paper, we investigate and compare themarket behavior of these sectors before and during the COVID period. All indices hadexperienced sudden volatility in March 2020 when the lockdown was announced, allthe sectors recovered from the losses during the financial year 2020-21 and exhibiteda positive ret

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Jasim, Al-Ajmi. "Trading volume and volatility in the Boursa Kuwait." British Accounting Review 2017, no. 10 (2017): 16. https://doi.org/10.5281/zenodo.1034509.

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This paper presents the results of a study of the effect of daily trading volume on the persistence of timevarying conditional volatility for Kuwait Stock Exchange. The sample includes the market index, seven sectoral indices and 20 stocks. Whereas inclusion of contemporaneous volume in the equation of conditional variance does not reduce the persistence of volatility for the eight indices, this is not the case for individual companies. Furthermore, the lagged intraday volatility has higher predictive power for volatility than the lagged trading volume. These results lend further support to th

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37

Fitriyah, Fitriyah, and Zakia Nur Mukminatin. "Return connectedness of sectoral stock indices on the Indonesian stock exchange during Russian-Ukraine conflict." Journal of Accounting and Investment 26, no. 1 (2025): 155–72. https://doi.org/10.18196/jai.v26i1.22406.

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Research aims: The purpose of this study is to determine the return connectedness of sectoral stock indices in Indonesia and to identify sectors that act as risk transmitters and risk recipients during the Russia-Ukraine conflict.Design/Methodology/Approach: The study employed a quantitative method with a comparative descriptive approach using data obtained from id.investing.com in the form of daily data on the closing price of the sectoral stock index starting from the period March 16, 2021 to March 14, 2023. The sampling technique used purposive sampling method. Time varying parameter VAR is

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38

Lawrence, Arun, Gabriel Simon Thattil, Tom Antony, and Jeena Ittoop. "Stock Market Movements - A Comparative Analysis on Growth Patterns in India’s Leading Stock Exchange." International Journal of Economics and Financial Issues 15, no. 2 (2025): 235–43. https://doi.org/10.32479/ijefi.17865.

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Growth in the financial market need not always get reflected in terms of earnings growth for investors. Certain sectors and certain class of investors could remain beyond the growth story. It is thus essential to examine whether growth in the Indian financial market are reflected in terms of real time gains in leading financial indices. The leading stock market institution of the country namely National Stock Exchange (NSE) and its sectoral indices from the subject matter of analysis. This study analyses the gains and performance trends of the Indian financial market over the past decade, focu

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Sutrisno, Bambang. "The Impact of Macroeconomic Variables on Sectoral Indices in Indonesia." ETIKONOMI 16, no. 1 (2017): 71–80. http://dx.doi.org/10.15408/etk.v16i1.4323.

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This study aims to examine the effect of macroeconomic variables on sectoral indices in the Indonesian Stock Exchange. The difference in sensitiveness among sectors is an interesting issue to investigate this relationship in an emerging market, such as Indonesia. This study employs ordinary least square (OLS) as an estimation method with monthly time-series data from January 2005 to December 2014. The results document that the interest rate, inflation rate, and exchange rate simultaneously have a significant effect on sectoral indices in Indonesia. The interest rate partially shows a significa

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Chauhan, Sanjay Singh, Pradeep Suri, Bhekisipho Twala, Neeraj Priyadarshi, and Farman Ali. "Exploring the relationship between macroeconomic indicators and sectoral indices of Indian stock market." F1000Research 14 (April 7, 2025): 180. https://doi.org/10.12688/f1000research.160668.2.

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Background of the study The influence of macroeconomic indicators makes it important to study the relationship between macroeconomic indicators and stock market return. On further analysis it can be observed that different sectors respond differently to change in the macroeconomic indicator that is important for investors, researchers and policy makers. Methods The autoregressive distributed lag (ARDL) model is applied to study influence of macroeconomic indicators on sectoral return of NSE from April 2012 to August 2024. Results Findings of the study show that macroeconomic indicators influen

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Chauhan, Sanjay Singh, Pradeep Suri, Bhekisipho Twala, Neeraj Priyadarshi, and Farman Ali. "Exploring the relationship between macroeconomic indicators and sectoral indices of Indian stock market." F1000Research 14 (February 10, 2025): 180. https://doi.org/10.12688/f1000research.160668.1.

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Анотація:

Background of the study The influence of macroeconomic indicators makes it important to study the relationship between macroeconomic indicators and stock market return. On further analysis it can be observed that different sectors respond differently to change in the macroeconomic indicator that is important for investors, researchers and policy makers. Methods The autoregressive distributed lag (ARDL) model is applied to study influence of macroeconomic indicators on sectoral return of NSE from April 2012 to August 2024. Results Findings of the study show that macroeconomic indicators influen

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42

Bhatia, Parul, Sudhi Sharma, Vaibhav Aggarwal, and Niyati Chaudhary. "Testing event-based day of the week anomaly and trading opportunities: Evidence from Indian sectoral indices." Investment Management and Financial Innovations 21, no. 2 (2024): 28–43. http://dx.doi.org/10.21511/imfi.21(2).2024.03.

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The study is an attempt to examine the day-of-the-week anomaly of fourteen Indian sectoral indices and identify profitable opportunities, considering multiple positive and negative events. The aim of this study is to analyze the day-of-the-week effect on fourteen Indian sectoral indices and find profitable opportunities while considering multiple events that have positive and negative impacts. The study takes into consideration event-based anomalies, both national and global, and provides timing for trading to generate abnormal returns from the market. At first, dummy variable regression analy

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43

Mandal, Koushik, and Radhika Prosad Datta. "Analysing Time-frequency Relationship between Oil price and Sectoral Indices in India using Wavelet Techniques." International Journal of Energy Economics and Policy 12, no. 5 (2022): 192–201. http://dx.doi.org/10.32479/ijeep.13391.

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Oil is considered an essential factor of any economy. This paper examines the time-varying correlation between oil price return, BSE SENSEX, and 14 sectoral indexes in India using multiscale wavelet decomposition and wavelet coherence analysis. The maximal wavelet discrete wavelet transform analysis shows a feedback relationship between 13 sectors at higher time horizons (dC4, dC5, and dC6). Based on the wavelet coherence plot, the oil price and sectoral index return show a high co-movement at 32 to 128 weeks. The wavelet coherence plot shows that the oil price and sectoral index return show a

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44

M., Manimaran, and N. Vijai Anand Dr. "ANALYSIS ON RETURN, RISK AND VOLATILITY OF SECTORAL INDICES AGAINST BSE." International Journal of Current Research and Modern Education 2, no. 1 (2017): 265–69. https://doi.org/10.5281/zenodo.1044498.

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BSE has classified industrial grouping based on similar production processes, products or nature of business. Automobile, Banking, Energy, Fast moving Consumer Goods (FMCG), Healthcare, Industrials, IT, Oil & Gas, Power, Public Sector Undertakings (PSU) and Telecom are the classifications. This paper analyzes the return, risk and volatility of such sectoral indices against BSE for the period 2007:01 to 2016:12. The objectives of the study are “ To classify sectors based on Return (High, Medium and Low)”, “To classify sectors based on sensitivity (High, Medium and low), “To classify sectors

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Meo, Muhammad Saeed, Farah Durani, Sajid Ali, Alade Ayodeji Ademokoya, Raima Nazar, and Syed Ali Raza. "Performance of sectoral Islamic indices during COVID-19." International Journal of Trade and Global Markets 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijtgm.2021.10040255.

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46

Colaco, Faye Xavier, Andrea Almeida D'Costa, and K. T. Rangamani. "Understanding the NSE Sectoral Indices through Beta Analysis." FIIB Business Review 3, no. 2 (2014): 57–61. http://dx.doi.org/10.1177/2455265820140210.

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Meo, Muhammad Saeed, Farah Durani, Sajid Ali, Alade Ayodeji Ademokoya, Raima Nazar, and Syed Ali Raza. "Performance of sectoral Islamic indices during COVID-19." International Journal of Trade and Global Markets 16, no. 4 (2022): 301. http://dx.doi.org/10.1504/ijtgm.2022.128352.

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Zhang, Yanjia, Shih-tse Lo, and Dhanoos Sutthiphisal. "Inter-Market Mean and Volatility Spillover Dynamics Between Cryptocurrencies and an Emerging Stock Market: Evidence from Thailand and Sectoral Analysis." Risks 13, no. 4 (2025): 77. https://doi.org/10.3390/risks13040077.

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The increasing interaction between the equity market and cryptocurrencies has raised concerns about volatility spillovers; however, empirical evidence about sectoral-specific spillover effects in emerging markets is scarce and hard to find. Existing research mainly concentrates on developed markets and aggregate equity indices, leaving a research gap in comprehending how sectoral indices variations impact market interactions in developing financial markets like Thailand. This article investigates the mean and volatility spillover effects between the Thai stock market and leading cryptocurrenci

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49

Islam, Khalid Ul, and M. M. Goyal. "Examining the Fisher Effect in Short and Long Run: A Study of NSE Sectoral Indices." GIS Business 12, no. 5 (2017): 20–29. http://dx.doi.org/10.26643/gis.v12i5.3341.

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The belief that stock market provides hedge against inflation has been put to test by many researchers over the past few decades. The present study aims at testing the Fisher effect in the Indian context. We have used monthly data, from July 2006 to June 2016, of the National Stock Exchange sectoral indices and consumer price index. The ordinary least square regression and Johansen cointegration approach have been used to test whether or not Indian sectoral indices provide hedge against inflation in short and long run respectively. The weak exogenity test under VECM has been used to establish

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Hosen, Mosharrof, Mohammed Imran, and Mohammad Ashraful Ferdous Chowdhury. "Nexus Between Sectoral Shift and Stock Return." International Journal of Asian Business and Information Management 12, no. 1 (2021): 75–93. http://dx.doi.org/10.4018/ijabim.20210101.oa5.

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The objective of this study is to examine the impact of sectoral shift on the stock return of Bangladesh. This study employs auto-regressive distributive lag (ARDL) approach using the weekly data of various sectoral indices of Bangladesh over the period from May 1999 to September 2016. The findings tend to indicate that there has possible sectoral portfolio diversification in the market and ‘general product industry' is the most exogenous and profitable sector from the rest. This study is one of the first attempts of the sectoral analysis and its impact on the stock return with the reference t

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