4 datasets found
  1. f

    38 Global main stock indexes.

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Bentian Li; Dechang Pi (2023). 38 Global main stock indexes. [Dataset]. http://doi.org/10.1371/journal.pone.0200600.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bentian Li; Dechang Pi
    License

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

    Description

    This is the name of the 38 global main stock indexes in the world. We collected from Yahoo! Finance. For the convenience of expression and computation later, we numbered it. For each item, the front is its serial number, followed by the corresponding stock index.

  2. 34-year Daily Stock Data (1990-2024)

    • kaggle.com
    Updated Dec 10, 2024
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    Shivesh Prakash (2024). 34-year Daily Stock Data (1990-2024) [Dataset]. https://www.kaggle.com/datasets/shiveshprakash/34-year-daily-stock-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 10, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Shivesh Prakash
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset Description: 34-year Daily Stock Data (1990-2024)

    Context and Inspiration

    This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

    Sources

    The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

    Columns

    1. dt: Date of observation in YYYY-MM-DD format.
    2. vix: VIX (Volatility Index), a measure of expected market volatility.
    3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
    4. sp500_volume: Daily trading volume for the S&P 500.
    5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
    6. djia_volume: Daily trading volume for the DJIA.
    7. hsi: Hang Seng Index, representing the Hong Kong stock market.
    8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
    9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
    10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
    11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
    12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
    13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

    Key Features

    • Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
    • Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
    • Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

    Potential Use Cases

    • Forecasting market indices using machine learning or statistical models.
    • Building volatility trading strategies with VIX Futures.
    • Economic research on relationships between policy uncertainty and market behavior.
    • Educational material for financial data visualization and analysis tutorials.

    Feel free to use this dataset for academic, research, or personal projects.

  3. T

    Baltic Exchange Dry Index - Price Data

    • tradingeconomics.com
    • ru.tradingeconomics.com
    • +14more
    csv, excel, json, xml
    Updated May 26, 2017
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    TRADING ECONOMICS (2017). Baltic Exchange Dry Index - Price Data [Dataset]. https://tradingeconomics.com/commodity/baltic
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    May 26, 2017
    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
    Jan 4, 1985 - Sep 1, 2025
    Area covered
    World
    Description

    Baltic Dry fell to 2,024 Index Points on September 1, 2025, down 0.05% from the previous day. Over the past month, Baltic Dry's price has risen 2.74%, and is up 5.47% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on September of 2025.

  4. T

    Coffee - Price Data

    • tradingeconomics.com
    • de.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Sep 1, 2025
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    TRADING ECONOMICS (2025). Coffee - Price Data [Dataset]. https://tradingeconomics.com/commodity/coffee
    Explore at:
    csv, json, excel, xmlAvailable download formats
    Dataset updated
    Sep 1, 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
    Aug 16, 1972 - Sep 1, 2025
    Area covered
    World
    Description

    Coffee rose to 398.06 USd/Lbs on September 1, 2025, up 0.30% from the previous day. Over the past month, Coffee's price has risen 37.95%, and is up 63.78% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on September of 2025.

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Email
Click to copy link
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Close
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Bentian Li; Dechang Pi (2023). 38 Global main stock indexes. [Dataset]. http://doi.org/10.1371/journal.pone.0200600.t001

38 Global main stock indexes.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
May 31, 2023
Dataset provided by
PLOS ONE
Authors
Bentian Li; Dechang Pi
License

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

Description

This is the name of the 38 global main stock indexes in the world. We collected from Yahoo! Finance. For the convenience of expression and computation later, we numbered it. For each item, the front is its serial number, followed by the corresponding stock index.

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