2 datasets found
  1. 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.

  2. Facebook users worldwide 2017-2027

    • statista.com
    • es.statista.com
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    Stacy Jo Dixon, Facebook users worldwide 2017-2027 [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Shivesh Prakash (2024). 34-year Daily Stock Data (1990-2024) [Dataset]. https://www.kaggle.com/datasets/shiveshprakash/34-year-daily-stock-data
Organization logo

34-year Daily Stock Data (1990-2024)

Common stocks' daily closing values and trading volumes for quick ML projects

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.

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