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Статті в журналах з теми "Sectoral Index"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Mishra, Pallavi. "Cyclical Patterns in BSE Sectoral Index." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 152–58. http://dx.doi.org/10.5373/jardcs/v11sp10/20192786.

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2

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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3

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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4

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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5

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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6

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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7

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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8

Safitri, Yunita Dewi, and Robiyanto Robiyanto. "KORELASI DINAMIS ANTARA PERGERAKAN HARGA MINYAK DUNIA DAN INDEKS HARGA SAHAM SEKTORAL DI BURSA EFEK INDONESIA." Jurnal Ekonomi Bisnis dan Kewirausahaan 9, no. 3 (2020): 188. http://dx.doi.org/10.26418/jebik.v9i3.42949.

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Changes in the situation that move very quickly on the commodity market have an impact on financial markets, one of which is the stock market in Indonesia. Therefore this study aims to examine the dynamic correlation between the movement of world oil prices and the Sectoral Stock Price Index listed on the Indonesia Stock Exchange (IDX). The data used is obtained from secondary data in the form of daily closing price data for world oil prices and Sectoral Stock Price Index from January 2017 to June 2020. The analysis technique used is Dynamic Conditional Correlation-Generalized Autoregressive C

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9

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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10

Safitri, Heni. "Black Swan Event on JCI Value and Sectoral Index for February-April 2020: Effects of Covid-19 in Indonesian Stock Exchange." Journal of Business and Management Studies 4, no. 1 (2022): 57–63. http://dx.doi.org/10.32996/jbms.2022.4.1.8.

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The purpose of this study is to determine the effect or impact of the Black Swan Event (Covid19) on the value of the IHSG and Sectoral Index on the IDX and how much the comparison of changes in the value of the IHSG and Sectoral Index before and after the existence of Covid19. This research is included in associative research. Data collection techniques used documentation study. The samples in this study were all IHSG values and all Sectoral Indices on the IDX. The analytical tool used in this research is Structural Equation Modeling (SEM) with the WarpPLS Approach and the Wilcoxon Signed Ra

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11

Pizhuk, Olha, Larysa Lazebnyk, and Hanna Mamonova. "Digitalization’s Effect on the Sectoral Structure Change in the Economy: a Comparative Analysis of Ukraine and Selected Countries." Comparative Economic Research. Central and Eastern Europe 25, no. 2 (2022): 21–43. http://dx.doi.org/10.18778/1508-2008.25.11.

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This article examines the impact of digital transformation on changes in the sectoral structure of Ukraine’s economy, other post‑communist countries, and countries with highly developed economies. Firstly, we estimate the structural changes and differences in the sectoral model by gross value‑added and sectoral employment model by country using Ryabtsev’s index. Secondly, we calculated the forecast of changes in the researched economies' sectoral structures for 2021 and 2025 using discrete Markov processes. The forecasts made it possible to determine the direction of socio‑economic progress of

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12

Limor, Andre, and Hendra Wiyanto. "Dampak Variabel Makro Ekonomi Terhadap Indeks Harga Saham Sektor Properti Di Bei." Jurnal Manajerial Dan Kewirausahaan 1, no. 2 (2019): 118. http://dx.doi.org/10.24912/jmk.v1i2.5070.

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This study aims to examine the influence of inflation rate, US$ exchange rate, BI interest rate on sectoral index of property stock price changes 2014-2017. This study is using Eviews 9.0 with causal (experimental) study design. The results of this study indicate inflation rate, BI interest rate have no significant influences on sectoral index of property stock price, meanwhile, US$ exchange rate has a significant influence on sectoral index of property stock price.Penelitian ini bertujuan untuk menguji pengaruh tingkat inflasi, kurs USD, tingkat suku bunga acuan BI terhadap indeks harga saham

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13

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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14

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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15

ULUCEVİZ, Erhan. "Borsa Istanbul Sectoral Connectedness Analysis." Yönetim ve Ekonomi Dergisi 31, no. 1 (2024): 61–74. http://dx.doi.org/10.18657/yonveek.1284521.

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This study examines the volatility connectedness among banks, industrials, and services subindices of Borsa Istanbul using the Diebold-Yılmaz connectedness index methodology. The findings indicate that the banks index typically acts as a net receiver of connectedness from industrials and services indices. If the banks index is considered a proxy for the financial side of the Turkish economy while the other two represent the real side, this result aligns with earlier observations on the connectedness between the real and financial sides of economies. Specifically, it suggests that when a proxy

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16

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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17

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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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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18

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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19

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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20

Andamari, Basis G., Shanti Lysandra, and Salis Musta Ani. "Pengaruh pandemi Covid-19 terhadap indeks sektoral di Bursa Efek Indonesia." AKURASI: Jurnal Riset Akuntansi dan Keuangan 4, no. 3 (2022): 191–200. https://doi.org/10.36407/akurasi.v4i3.696.

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Анотація:

This study analyzes the effect of the Covid-19 pandemic, which consists of the number of confirmed positives, the number of active people, and the number who died from Covid-19 on the sectoral index on the Indonesia Stock Exchange (IDX). The research method used is quantitative research with causality analysis. The data used in this study is secondary data obtained from the Ministry of Health website and Worldometers for the Covid-19 pandemic and the IDX for sectoral index data for the period January 2020-June 2021. The results of the analysis show that the Covid-19 pandemic affects all IDX se

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21

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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22

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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23

Baariu, Mungiria, and Ambrose Jagongo. "MACROECONOMIC VARIABLES, SECTORAL INDEX VOLATILITY, AND INVESTOR SENTIMENT AMONG LISTED FIRMS AT NAIROBI SECURITY EXCHANGE, KENYA." International Journal of Finance and Accounting 7, no. 1 (2022): 61–75. http://dx.doi.org/10.47604/ijfa.1475.

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Purpose: From a broader perspective, it is generally accepted that every investor aims to maximize return on their investment. To achieve this, the security market has significantly attracted so much interest from numerous stakeholders around the globe. However, it is difficult to forecast the stock index volatility exhaustively since it is triggered by different factors, which erode the investors’ confidence. The impact of volatility in the stock market is not the same (Liu et al., 1998), and it is transmitted from one sector to the other. Therefore, this study seeks to establish the relation

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24

Rahmat Budi Santoso, Erawati Kartika, and Ika Listyawati. "Portfolio Diversification Opportunities On The Asean 5 Stock Market And Sectoral Stock Indexes On The Indonesian Stock Exchange." INTERNATIONAL CONFERENCE ON DIGITAL ADVANCE TOURISM, MANAGEMENT AND TECHNOLOGY 1, no. 1 (2023): 155–65. http://dx.doi.org/10.56910/ictmt.v1i1.59.

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 This study aims to examine portfolio diversification opportunities on the stock markets of Indonesia, Singapore, Malaysia, Thailand, and the Philippines, as well as sectoral stock price indices on the Indonesian Stock Exchange. The data used is the closing daily price index for the period January 2021 – October 2023. The analysis method uses principal component analysis. The results show that the first forming component includes the Indonesian stock market and its sectoral stock price index. The second component is filled by the stock markets of Singapore, Malaysia, Thailand, and the Ph

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25

Miahkykh, Iryna, and Viacheslav Kokhno. "SECTORAL DISPARITIES IN DIGITAL TRANSFORMATION: A COMPARATIVE ANALYSIS OF UKRAINE’S ECONOMIC SECTORS USING A COMPOSITE INDEX APPROACH." Смарт-економіка, підприємництво та безпека 3, no. 1 (2025): 64–71. https://doi.org/10.60022/sis.3.(01).7.

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Digital transformation is a key driver of economic modernization, yet its progress remains uneven across sectors, particularly in emerging economies like Ukraine. This study develops a composite Digital Trans- formation Index to assess and compare the level of digitalization across nine major economic sectors in Ukraine, including ICT, Finance, Healthcare, Education, Agriculture, and others. The index integrates four core dimensions: digital infrastructure and access, human capital and skills, technology adoption and integration, and digital innovation and investment. Results reveal pronounced

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26

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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27

Espa, Vitriyan, Eddy Suratman, and Wahyudi. "Empirical investigation on sectoral inequality, gender empowerment, education, and income inequality in Indonesia: Dynamic panel approach." Asian Development Policy Review 13, no. 1 (2024): 25–37. https://doi.org/10.55493/5008.v13i1.5245.

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Sectoral inequality exemplifies the baseline condition; despite the same significant sectoral growth, high sectoral inequality also leads to high income inequality. This is because sectors with low contributions and sectors with high contributions to regional income will provide very unequal income for the workforce involved in them. Each variable may have a different relationship in the long term. This study aimed to investigate the short-term and long-term effects of Sectoral Inequality, Gender Empowerment Index (GEI), and Average Years of Schooling (AYS) on Income Inequality in Indonesia. T

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28

Panagiotidis, Theodore, and Gianluigi Pelloni. "Asymmetry and Lilien’s Sectoral Shifts Hypothesis: A Quantile Regression Approach." Review of Economic Analysis 6, no. 1 (2014): 68–86. http://dx.doi.org/10.15353/rea.v6i1.1413.

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This study revisits Lilien’s sectoral shifts hypothesis for the US. We employ quantile regression estimation in order to investigate the asymmetric nature of the relationship between sectoral employment and unemployment. Significant asymmetries emerge. Lilien’s dispersion index is significant only for relatively high levels of unemployment and becomes insignificant for lower levels suggesting that reallocation affects unemployment only when the latter is relative high. More job reallocation is associated with higher unemployment.

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29

Mahat, Fauziah, Aminah Shari, and Ahmed Mohamed Dahir. "An Analysis of the Relationship Between Equity and Composite Index in Bursa Malaysia." Indian-Pacific Journal of Accounting and Finance 4, no. 1 (2020): 4–14. http://dx.doi.org/10.52962/ipjaf.2020.4.1.95.

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This paper examines the relationship between sectoral equity and composite index in Bursa Malaysia. The relationship between equity returns and composite index is investigated using correlation-based on Ordinary Least Square (OLS) and signal decomposition techniques based on wavelet analysis. The paper uses daily data from 1999 to 2019. The OLS result indicated that majority of sectoral equity have a higher correlation with the composite index except in tin and mining sector. The wavelet analysis indicates a majority of sectors are strongly co-move. For all indices, there are lead/lag relation

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30

Louhenapessy, Desry Jonelda. "ANALYSIS OF CHANGES IN THE SECTORAL ECONOMIC STRUCTURE THROUGH CALCULATION OF AMBON CITY STRUCTURAL CHANGE INDEX IN 2015-2020." Jurnal Cita Ekonomika 15, no. 1 (2021): 50–60. http://dx.doi.org/10.51125/citaekonomika.v15i1.3490.

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By using the Structural Change Index, this study aims to analyze changes in the economic structure of Ambon City for period 2015-2020. From this analysis it can be concluded; Based on the calculation of the Structural Change index, there has been a change in the economic structure of Ambon City which is marked by the shift in the contribution of the primary sector to the secondary and tertiary sectors in the Ambon City economy. The change by a sectoral shift from the primary sector to the secondary sector to the tertiary sector with an index of 0.64 percent, this means 0.64 percent of ec

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31

Widarjono, Agus, and Mustika Mifrahi. "SECTORAL FINANCING CONCENTRATION AND SHARIA RURAL BANKS’ PROFITABILITY." Jurnal Ekonomi dan Bisnis Islam (Journal of Islamic Economics and Business) 10, no. 2 (2024): 265–88. https://doi.org/10.20473/jebis.v10i2.59121.

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Introduction: Sharia rural banks (SRBs) in Indonesia face high non-performing financing (NPF). The high NPF likely causes a decrease in the profitability of SRBs. One strategy to overcome high financing risk is selecting the appropriate financing strategy either sectoral financing diversification or concentration. This study explores the impact of sectoral financing concentration along with some control variables on the profitability of Sharia rural banks in Indonesia using aggregate data of SRBs from 2010:M1 to 2023:M12 Methods: Our study employs the quantitative method utilizing the Autoregr

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Dolge, Kristiāna, Anna Kubule, and Dagnija Blumberga. "Composite index for energy efficiency evaluation of industrial sector: sub-sectoral comparison." Environmental and Sustainability Indicators 8 (December 2020): 100062. http://dx.doi.org/10.1016/j.indic.2020.100062.

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Sun, Pui Man, and Hock Tsen Wong. "STOCK PRICE AND MACROECONOMIC VARIABLES: AN EMPIRICAL ANALYSIS OF MALAYSIA AND HONG KONG SECTORAL STOCK INDICES." Labuan Bulletin of International Business and Finance (LBIBF) 17, no. 1 (2019): 157–89. http://dx.doi.org/10.51200/lbibf.v17i1.1920.

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This study employs the autoregressive distributed lag (ARDL) bounds testing approach to investigate the relationship between sectoral stock indices and selected macroeconomic variables (overnight interbank interest rate, consumer price index (CPI), money supply M1, exchange rate and industrial production index (IPI)) in Malaysia and Hong Kong for the period of May 2000 to June 2017. The S&P 500 index is also included to capture its influence on these stock markets. The procedures performed include the unit root test, ARDL bounds test for cointegration, estimations of long-run coefficients

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Mzumara, Macleans, Anna Chingarande, and Roseline Karambakuwa. "Intra-Sectoral Competitiveness of Angola." Greener Journal of Economics and Accountancy 2, no. 3 (2013): 74–82. https://doi.org/10.15580/gjea.2013.3.082613805.

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The study investigated intra-sectoral competiveness of Angola. Using Balassa (1965) index measure, competitiveness was identified in Angola’s 12 sectors. The most competitive sector was found to be textiles with 121 product lines with revealed comparative advantage (RCA)≥1. It is followed by metals sector with 50 product lines in it. The third competitive sector is chemicals and allied industries with 48 product lines. The least competitive sector is transportation with only two product lines. It is followed by miscellaneous sector with only four product lines. There was no competitiv

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Annathurai, Anusha. "COVID 19 Outbreak and Tokyo 2020 Olympic Games: A Perspective from Japan’s Economy and Capital Market." Journal of Entrepreneurship and Business 10, no. 2 (2022): 144–60. http://dx.doi.org/10.17687/jeb.v10i2.924.

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This study highlights the key impacts of hosting an international sports event, Tokyo 2020 Olympic Games, during the period of deadliest contagious disease outbreak in history, COVID 19. The perspective is analysed in terms of the performance of capital market, investment market and sectoral market by the indexes of TOPIX, Stock Market, Bond Market, Foreign Exchange, FTSE, MSCI, and sectoral index of Air Transportation, Construction, Pharmaceutical which shows different range of fluctuation.

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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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DR.S.RAJAMOHAN, DR S. RAJAMOHAN, and M. MUTHUKAMU M.MUTHUKAMU. "Bank Nifty Index and Other Sectoral Indices of NSe- A Comparitive Study." Paripex - Indian Journal Of Research 3, no. 4 (2012): 147–49. http://dx.doi.org/10.15373/22501991/apr2014/47.

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Kovtun, Oksana, Alla Opalenko, and Oksana Ivanylova. "Assessment of the structural changes of the national economy of Ukraine based on the consistency." SHS Web of Conferences 65 (2019): 04002. http://dx.doi.org/10.1051/shsconf/20196504002.

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This article deals with the assessment of the structural changes of the national economy of Ukraine, which proposed to implement by applying the integrated index of structural changes, assessment of the structural changes of the sectoral structure of GDP, at determining the weighting coefficients and construction which uses the “golden ratio” rule. This approach is based on the theoretical completion of the economics as for the progressive development and transformation of society and the economy of the country according to the technological criterion, as well as the sectoral division of the n

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Shmygol, Nadiia, Oleksii Solovyov, Maja Kasianok, Olena Cherniavska, and Dariusz Pawliszczy. "Model of sectoral competitiveness index by environmental component." IOP Conference Series: Earth and Environmental Science 628 (January 23, 2021): 012023. http://dx.doi.org/10.1088/1755-1315/628/1/012023.

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Cosrojas, Karen Debbie J. "Public Spending, Outcomes and Governance in ASEAN Economic Community." IAR Journal of Humanities and Social Science 3, no. 02 (2022): 69–76. http://dx.doi.org/10.47310/iarjhss.2022.v03i02.010.

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The study explored the link of sectoral public spending to development outcomes with the interaction of governance using panel data from nine countries of the ASEAN economic community from 1995 to 2013. Using path analysis, result shows that the total effect of sectoral public spending such as health, and investment spending to human development index is significant but negative and the direct effect of education spending to HDI is having a positive but non-significant path. These imply that sectoral spending does not directly effect to development outcome. Meanwhile, the direct effect paths f

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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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Volokhin, E. A., and S. P. Syrygin. "THE IMPACT OF MACROECONOMIC FACTORS ON THE SYSTEMATIC RISK OF THE RUSSIAN STOCK MARKET." Social’no-ekonomiceskoe upravlenie: teoria i praktika 21, no. 1 (2025): 18–28. https://doi.org/10.22213/2618-9763-2025-1-18-28.

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The article is dedicated to the characteristics of systematic risk in the Russian stock market. It provides an analysis of macroeconomic factors influencing the Russian stock market, highlighting the causal relationships between economic factors and market dynamics. These factors include exchange rates, oil prices, inflation, the monetary policy of the Central Bank of Russia, the M2 money supply, and geopolitical factors. The sample of daily data covers the period from December 13, 2019, to May 6, 2024. Using the Asymmetric Generalized Autoregressive Conditional Heteroskedasticity model with D

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Hasnat, Tanzeem. "Infrastructure as an Investment: A Study of Listed Infrastructure Firms in India." Indian Economic Journal 66, no. 3-4 (2018): 400–402. http://dx.doi.org/10.1177/0019466220935566.

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The important task of infrastructure provision and enhancement faces ever rising financing requirement in the face of little fiscal space and this points to the pertinence of market based finance. This calls for a pressing need to gauge the sectoral performance of infrastructure and this study takes up this task. The study assesses the risk-return and volatility profile of Nifty Infra, the National Stock Exchange (NSE) sectoral index for infrastructure vis-à-vis the broader Nifty 50 for the time period 2010-2019. The standard financial economic analysis finds the sectoral equity performance of

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Blajer-Gołębiewska, Anna, and Sabina Nowak. "Do their reputations precede them? Stock market reaction to changes in corporate reputation in the context of sector and market maturity." JOURNAL OF INTERNATIONAL STUDIES 17, no. 1 (2024): 52–82. http://dx.doi.org/10.14254/2071-8330.2024/17-1/4.

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Our study extends the existing literature by examining whether the stock market’s maturity and the sector in which a company operates affect the relationship between an improvement in its corporate reputation and its stock returns. This event-study research is based on data from developed and emerging stock markets: the NYSE (US) and the WSE (Poland). The improvement in corporate reputation is proxied by its inclusion in a reputational index. We analysed inclusions in the RESPECT Index and WIG-ESG in Poland from 2009 to 2023. Then we compared the effects of inclusions on stock prices in the Po

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Khan, Abbas, Muhammad Yar Khan, and Abdul Qayyum Khan. "Do the electricity price shocks influence the Sectoral Production and KSE100 Index in Pakistan? An ARDL structural breaks approach." Journal of Applied Economics and Business Studies 5, no. 1 (2021): 17–46. http://dx.doi.org/10.34260/jaebs.512.

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This research investigates the long-term cointegration of electricity price with sectoral production and equity market in Pakistan. Fourteen major industrial sectors and the KSE100 index is taken into consideration to determine the relationship. Literature in this regard is available but this research is distinct from previous literature for it tests the sectoral production and equity market relationship with electricity price change in Pakistan. Monthly data from 1st Jan 2011 till 31st Dec 2019 is taken for fourteen sectors from the sources of Quantum Index Pakistan Bureau of Statistics (PBS)

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Barbić, Tajana, and Iva Čondić-Jurkić. "Viral Trends and Stock Markets: Spillover Between Meme Assets and Sectoral Returns." Financial Internet Quarterly 20, no. 4 (2024): 1–15. https://doi.org/10.2478/fiqf-2024-0023.

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Abstract Meme assets are a unique and modern phenomenon in the stock market, characterized by social media-driven hype and significant price volatility. The aim of this paper is to explore the relationships between meme assets and sectoral dynamics. We employ the Granger causality test to examine predictive relationships between daily returns of GameStop and five meme exchange traded funds and eleven sector index funds. Our results show that selected meme assets have relatively limited impact on various sectoral indices and vice versa, suggesting that meme stocks and meme ETFs can offer divers

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Borozan, Dj, and B. Borozan. "Analysing an Energy Sectoral Specialisation Pattern in Southern European Union Countries." IOP Conference Series: Earth and Environmental Science 952, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1755-1315/952/1/012009.

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Abstract Exploring the sectoral energy structure of Southern European Union (EU) countries of the Mediterranean region, the paper investigates the changes that have occurred in the distribution of the major energy end-use sectors in comparison to the EU average. To that end, the indices of sectoral specialisation were calculated, and then the behavioural pattern of energy users was analysed. The results indicate a relatively low sectoral heterogeneity of final energy consumption in the sample of interest, meaning that the structure of energy end-use sectors seems to show a rather comparable de

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Белова, И. А. "INTENSITY OF STRUCTURAL SHIFTS AND DIFFERENCES IN THE ECONOMY OF THE SUBJECTS OF THE URAL FEDERAL DISTRICT." Surgut State University Journal 11, no. 3 (2023): 19–29. http://dx.doi.org/10.35266/2312-3419-2023-3-19-29.

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The differences and intensity of structural shifts in the sectoral structure of the gross value added of the economy of the subjects of the Ural Federal District are assessed using the Ryabtsev index. Five approaches to assessing such differences are proposed. The Ural Federal District’s sectoral structure differs by far from that of the Russian Federation. The study identifies the Ural Federal District’s subjects with the great-est and least similarity to the sectoral structure of the Ural Federal District as a whole, as well as those with the greatest and least differences from the sectoral

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Sarihastuti, Dewy. "Optimalisasi Penyelenggaraan Statistik Sektoral sebagai Upaya Pemenuhan Data Statistik Berkualitas di Indonesia." Jurnal Syntax Admiration 5, no. 10 (2024): 4343–63. http://dx.doi.org/10.46799/jsa.v5i10.1715.

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The development of data needs in Indonesia has increased awareness of the importance of quality data in supporting development planning and implementation. The government responded by issuing Presidential Regulation Number 39 of 2019 concerning One Data Indonesia (SDI), which aims to produce accurate, up-to-date, and accountable data. However, the implementation of sectoral statistics still faces challenges, including the low national Statistical Development Index (IPS) in 2023 which is in the less than ideal category with a value of 1.78 on a scale of 5.00. This study aims to identify the roo

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Capoani, Luigi, and Martini Piergiorgio. "The Role of Geographic and Sectoral Diversification, and the Herfindahl-Hirschman Index: Insights from Italian Provinces and Regions." Journal of Global Trade, Ethics and Law 2, no. 2 (2024): 73–106. https://doi.org/10.70150/wq5d0b20.

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This study examines the role of the Herfindahl-Hirschman Index (HHI) and a new diversification index that integrates geographical and sectoral factors to analyze economic activities in Italian provinces. The research highlights how local territorial heterogeneity influences urban and regional economics, emphasizing diversification as a strategy for growth. The findings underscore diversification’s importance in enhancing economic stability and resilience, while comparing it with specialization. Although it can boost competitive advantage through efficiency and innovation, specialization also i

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