5 datasets found
  1. T

    China Shanghai Composite Stock Market Index Data

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Dec 2, 2025
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    TRADING ECONOMICS (2025). China Shanghai Composite Stock Market Index Data [Dataset]. https://tradingeconomics.com/china/stock-market
    Explore at:
    xml, csv, excel, jsonAvailable download formats
    Dataset updated
    Dec 2, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 19, 1990 - Dec 2, 2025
    Area covered
    China
    Description

    China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

  2. f

    S1 Data -

    • figshare.com
    • plos.figshare.com
    xlsx
    Updated Jan 25, 2024
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    Xiaoyang Wang; Hui Guo; Muhammad Waris; Badariah Haji Din (2024). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0296712.s001
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    xlsxAvailable download formats
    Dataset updated
    Jan 25, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Xiaoyang Wang; Hui Guo; Muhammad Waris; Badariah Haji Din
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.

  3. External_Earnings_chin

    • kaggle.com
    zip
    Updated Jun 10, 2025
    + more versions
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    willian oliveira (2025). External_Earnings_chin [Dataset]. https://www.kaggle.com/datasets/willianoliveiragibin/external-earnings-chin
    Explore at:
    zip(469062 bytes)Available download formats
    Dataset updated
    Jun 10, 2025
    Authors
    willian oliveira
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This study uses panel data on Chinese A-share listed companies in Shanghai and Shenzhen covering 2014 to 2020 selected through the following screening: first, we exclude listed companies in the finance and insurance sectors; second, we exclude listed companies in ST and *ST (Special Treatment); finally, we exclude samples that lack important data. This approach generates 8,658 valid research sample observations. The data are obtained from several official websites, such as those for CSMAR (China Stock Market & Accounting Research Database), CNRDS (Chinese Research Data Services), and the Shanghai and Shenzhen stock exchanges.In this study, the descriptive and relevance of the final data was tested using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed. The final data were tested for descriptiveness and correlation using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed.

  4. Correlation matrix.

    • plos.figshare.com
    xls
    Updated Jan 25, 2024
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    Xiaoyang Wang; Hui Guo; Muhammad Waris; Badariah Haji Din (2024). Correlation matrix. [Dataset]. http://doi.org/10.1371/journal.pone.0296712.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 25, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Xiaoyang Wang; Hui Guo; Muhammad Waris; Badariah Haji Din
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.

  5. f

    Descriptive statistics of the sample from January 2006 to May 2020.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Mar 6, 2024
    + more versions
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    Xin Hu; Bo Zhu; Bokai Zhang; Lidan Zeng (2024). Descriptive statistics of the sample from January 2006 to May 2020. [Dataset]. http://doi.org/10.1371/journal.pone.0299237.t011
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 6, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Xin Hu; Bo Zhu; Bokai Zhang; Lidan Zeng
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Descriptive statistics of the sample from January 2006 to May 2020.

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TRADING ECONOMICS (2025). China Shanghai Composite Stock Market Index Data [Dataset]. https://tradingeconomics.com/china/stock-market

China Shanghai Composite Stock Market Index Data

China Shanghai Composite Stock Market Index - Historical Dataset (1990-12-19/2025-12-02)

Explore at:
15 scholarly articles cite this dataset (View in Google Scholar)
xml, csv, excel, jsonAvailable download formats
Dataset updated
Dec 2, 2025
Dataset authored and provided by
TRADING ECONOMICS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Dec 19, 1990 - Dec 2, 2025
Area covered
China
Description

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

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