14 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. F

    S&P 500

    • fred.stlouisfed.org
    json
    Updated Aug 29, 2025
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    (2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Aug 29, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval

    Description

    View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

  3. A New Index to Measure U.S. Financial Conditions

    • catalog.data.gov
    • s.cnmilf.com
    Updated Dec 18, 2024
    + more versions
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    Board of Governors of the Federal Reserve System (2024). A New Index to Measure U.S. Financial Conditions [Dataset]. https://catalog.data.gov/dataset/a-new-index-to-measure-u-s-financial-conditions
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    Dataset updated
    Dec 18, 2024
    Dataset provided by
    Federal Reserve Board of Governors
    Federal Reserve Systemhttp://www.federalreserve.gov/
    Description

    An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

  4. Predict the ASX-200

    • kaggle.com
    Updated Aug 25, 2021
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    YasAli (2021). Predict the ASX-200 [Dataset]. https://www.kaggle.com/datasets/yasali/predict-the-asx200/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 25, 2021
    Dataset provided by
    Kaggle
    Authors
    YasAli
    Description

    Disclaimer

    All information presented here is for display purpose only, and may not be complete nor accurate. This information does not constitute a financial advice, and should not be used to make any investment decisions or financial transactions. This author rejects any claims for liabilities resulting from the use, misuse, or abuse of this information. Use at your own risk.

    Motivation

    Due to time zone differences between Australia and most of the rest of the world, Australians have the advantage of knowing what happened at markets elsewhere in the world, before the Australian market (ASX) is open in the morning, Sydney time.

    This prior knowledge provides an excellent opportunity for arbitrage. In the hands of a savvy day-trader, or a shrewd long-term investor, this information gives you the advantage of predicting the ASX, and achieve potentially significant financial gains.

    Method

    For the ten years period from 1/7/2010 to 30/6/2020, the daily closing prices for 41 global market indicators are collected from various reliable public-domain sources. We checked the data for error or omissions and normalised all tabulated records in a format that facilitates further analysis and visulaisation.

    Those 41 market indicators are what we consider significant measures of various external factors that may affect the performance of the Australian Stock Market, as represented by the ASX200. Those indicators are:

    • Nine other major stock market indices from the USA, Europe, and Asia.

    • The exchange rate of the $AU against 10 world currencies that are most relevant to Australia's international trade.

    • Official interest rates by the RBA and the US Feds, as indicators of affinity of foreign funds to Australia.

    • Yield rates for governments-issued bonds by 10 countries from Western and Asian economies, as measures of relative availability of credit and cross-border investment. Bonds are grouped into "Short-term" (one year maturity) and "Long-term" (10 to 30 years maturity).

    • Since Australia's economy is mainly an exporter of raw materials, we include prices for commodities that are most traded by Australia, as indicators for potential profitability for various relevant sectors of the ASX.

    We feed relevant data to a machine learning model, which uses this data to extract heuristic parameters that are used to predict the ASX200 on daily basis, before market opens, and validates predictions at market close, with favourable results.

    For more information, please visit the Tableau viz at: https://public.tableau.com/app/profile/yasser.ali.phd/viz/PredictingAustralianStockMarket/Story

  5. T

    India Interest Rate

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Mar 15, 2017
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    TRADING ECONOMICS (2017). India Interest Rate [Dataset]. https://tradingeconomics.com/india/interest-rate
    Explore at:
    excel, xml, csv, jsonAvailable download formats
    Dataset updated
    Mar 15, 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
    Jul 10, 2000 - Aug 6, 2025
    Area covered
    India
    Description

    The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  6. 4

    Data from: Data underlying the publication: The impact of the Hamas-Israel...

    • data.4tu.nl
    zip
    Updated Nov 28, 2024
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    Jeroen Klomp (2024). Data underlying the publication: The impact of the Hamas-Israel conflict on the U.S. defense industry stock market return [Dataset]. http://doi.org/10.4121/d8deb768-0d23-4330-adf9-3506b641088e.v1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 28, 2024
    Dataset provided by
    4TU.ResearchData
    Authors
    Jeroen Klomp
    License

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

    Time period covered
    2023 - 2024
    Area covered
    United States
    Description

    This dataset facilitates an analysis of the impact of the recent Israel-Hamas conflict on the stock market performance of U.S. defense companies, as measured by the returns of defense-sector Exchange-Traded Funds (ETFs). The conflict is quantified using variables such as a binary "attack" indicator, casualty counts, and the intensity of Google search activity related to the war. Additionally, the dataset incorporates a comprehensive set of control variables, including interest rates, exchange rates, oil prices, inflation rates, and factors related to the Ukraine conflict, ensuring a robust framework for evaluating the effects of this geopolitical event.

  7. m

    Japan Real Estate Investment Corp - Dividend-Per-Share

    • macro-rankings.com
    csv, excel
    Updated Aug 10, 2025
    + more versions
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    macro-rankings (2025). Japan Real Estate Investment Corp - Dividend-Per-Share [Dataset]. https://www.macro-rankings.com/Markets/Stocks/8952-TSE/Key-Financial-Ratios/Dividends_and_More/Dividend-Per-Share
    Explore at:
    excel, csvAvailable download formats
    Dataset updated
    Aug 10, 2025
    Dataset authored and provided by
    macro-rankings
    License

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

    Area covered
    japan
    Description

    Dividend-Per-Share Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

  8. m

    Japan Real Estate Investment Corp - Begin-Period-Cashflow

    • macro-rankings.com
    csv, excel
    Updated Jul 31, 2025
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    macro-rankings (2025). Japan Real Estate Investment Corp - Begin-Period-Cashflow [Dataset]. https://www.macro-rankings.com/Markets/Stocks?Entity=8952.TSE&Item=Begin-Period-Cashflow
    Explore at:
    excel, csvAvailable download formats
    Dataset updated
    Jul 31, 2025
    Dataset authored and provided by
    macro-rankings
    License

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

    Area covered
    japan
    Description

    Begin-Period-Cashflow Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

  9. e

    The European economic growth after the Second World War - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Sep 25, 2018
    + more versions
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    (2018). The European economic growth after the Second World War - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/fc5e72cf-711b-5540-ab9b-82d893df6439
    Explore at:
    Dataset updated
    Sep 25, 2018
    Area covered
    Europe
    Description

    The current growing interest in the growth of the Western European economies between the end of World War II and the first oil crisis of 1973 is primarily due to the end of the Cold War and the subsequent demand for solutions for the economic problems of Central and Eastern European transition countries. It was and is discussed to what extent we could learn from the successful rebuilding of the Western European economies. In this context one area of special interest is the reconstruction of West Germany, closely accompanied by the principle of the social market economy. The recollection of this principle, and the call for a new Marshall Plan imply the idea that the Western European post-war boom in essence can be traced to a successful economic policy. It is shown how this assumption can stand up to a theoretical and empirical analysis. Using the new growth theory and the cointegration analysis both national (eg social market economy and Planification (i.e. macroeconomic framework development planning)) and international explanations (eg the Marshall Plan) of the so called ‘golden age’ are examined. It turns out that the impact of economic policies on economic growth must be put into perspective. In contrast, the importance of the different economic conditions of the countries for the explication of their growth process is underlined. Variables, inter alia: - Investment behavior of industry - Production and Export industry - Exchange Rates - Structure of the economies Data focus: Foreign trade structure, external value (foreign wholesale prices), export volume, industrial production, capital stock, long-term development (income, investment rates, openness, exchange rates), patents (patent applications in Germany, France). List of tables in the database HISTAT ZA: - Investment rates in four European countries (1880-1995) - Net fixed assets of the industry in Germany (1950-1968) - Sectoral Gross capital expenditures in Germany (1960-1976) - Sectoral Gross investment in France (1949-1965) - Export volume index of France and the Federal Republic of Germany (1950-1973) - Export volume in millions of current U.S. dollars (1951-1990) - Weighted exchange rate index in indirect rate (1950-1973) - Index of industrial production in Europe and North America (1950-1973) - Construction and equipment investment in Germany (1950-1968) - Investment rates in four European countries (1880-1995) - Sectoral gross and net capital stock in France (1950-1970) - Sectoral gross and net capital stock, investment in France (1950-1969) - Percentage of the French colonies in the French total exports (1950-1973) - Openness of four European economies (1880-1994) - Annual patent applications in the United States (1963-1995) - Real per capita income in Europe and the United States (1870-1992) - Regional structure of the French export value (1896-1973) - French sector gross investment (1960-1976) - Exchange rates in four European countries (1891-1995) Territory of investigation: Germany, France, further OECD-states. Sources: Publications of the official French and German statistics, publications of the OECD, USA and further states; scientific journals.

  10. T

    United Kingdom Interest Rate

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Aug 7, 2025
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    TRADING ECONOMICS (2025). United Kingdom Interest Rate [Dataset]. https://tradingeconomics.com/united-kingdom/interest-rate
    Explore at:
    json, csv, excel, xmlAvailable download formats
    Dataset updated
    Aug 7, 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
    Sep 20, 1971 - Aug 7, 2025
    Area covered
    United Kingdom
    Description

    The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  11. T

    Pakistan Interest Rate

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 5, 2025
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    TRADING ECONOMICS (2025). Pakistan Interest Rate [Dataset]. https://tradingeconomics.com/pakistan/interest-rate
    Explore at:
    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    May 5, 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
    Feb 3, 1992 - Jul 30, 2025
    Area covered
    Pakistan
    Description

    The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  12. T

    Euro US Dollar Exchange Rate - EUR/USD Data

    • tradingeconomics.com
    • it.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, Euro US Dollar Exchange Rate - EUR/USD Data [Dataset]. https://tradingeconomics.com/euro-area/currency
    Explore at:
    excel, csv, xml, jsonAvailable download formats
    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 31, 1957 - Sep 1, 2025
    Area covered
    Euro Area
    Description

    The EUR/USD exchange rate rose to 1.1708 on September 1, 2025, up 0.21% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 1.07%, and is up by 5.79% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on September of 2025.

  13. T

    India Inflation Rate

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Aug 12, 2025
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    TRADING ECONOMICS (2025). India Inflation Rate [Dataset]. https://tradingeconomics.com/india/inflation-cpi
    Explore at:
    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    Aug 12, 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
    Jan 31, 2012 - Jul 31, 2025
    Area covered
    India
    Description

    Inflation Rate in India decreased to 1.55 percent in July from 2.10 percent in June of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  14. T

    Japanese Yen Data

    • tradingeconomics.com
    • es.tradingeconomics.com
    • +12more
    csv, excel, json, xml
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    TRADING ECONOMICS, Japanese Yen Data [Dataset]. https://tradingeconomics.com/japan/currency
    Explore at:
    xml, csv, json, excelAvailable download formats
    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, 1971 - Aug 29, 2025
    Area covered
    Japan
    Description

    The USD/JPY exchange rate rose to 146.9530 on August 29, 2025, up 0.22% from the previous session. Over the past month, the Japanese Yen has strengthened 1.52%, but it's down by 0.54% over the last 12 months. Japanese Yen - values, historical data, forecasts and news - updated on August of 2025.

  15. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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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
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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