80 datasets found
  1. M

    Dow Jones - 10 Year Daily Chart

    • macrotrends.net
    csv
    Updated Jun 30, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    MACROTRENDS (2025). Dow Jones - 10 Year Daily Chart [Dataset]. https://www.macrotrends.net/1358/dow-jones-industrial-average-last-10-years
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Time period covered
    1915 - 2025
    Area covered
    United States
    Description

    Interactive chart illustrating the performance of the Dow Jones Industrial Average (DJIA) market index over the last ten years. Each point of the stock market graph is represented by the daily closing price for the DJIA. Historical data can be downloaded via the red button on the upper left corner of the chart.

  2. T

    United States Stock Market Index Data

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +12more
    csv, excel, json, xml
    Updated May 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    TRADING ECONOMICS (2025). United States Stock Market Index Data [Dataset]. https://tradingeconomics.com/united-states/stock-market
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    May 15, 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 3, 1928 - Jun 24, 2025
    Area covered
    United States
    Description

    The main stock market index of United States, the US500, rose to 6074 points on June 24, 2025, gaining 0.80% from the previous session. Over the past month, the index has climbed 2.57% and is up 11.05% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

  3. Stock Portfolio Data with Prices and Indices

    • kaggle.com
    Updated Mar 23, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Nikita Manaenkov (2025). Stock Portfolio Data with Prices and Indices [Dataset]. http://doi.org/10.34740/kaggle/dsv/11140976
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 23, 2025
    Dataset provided by
    Kaggle
    Authors
    Nikita Manaenkov
    License

    https://www.gnu.org/licenses/gpl-3.0.htmlhttps://www.gnu.org/licenses/gpl-3.0.html

    Description

    This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

    1. Portfolio

    This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

    • Columns:
      • Ticker: The stock symbol (e.g., AAPL, TSLA)
      • Quantity: The number of shares in the portfolio
      • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
      • Close: The closing price of the stock
      • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

    2. Portfolio Prices

    This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

    • Columns:
      • Date: The date of the data point
      • Ticker: The stock symbol
      • Open: The opening price of the stock on that day
      • High: The highest price reached on that day
      • Low: The lowest price reached on that day
      • Close: The closing price of the stock
      • Adjusted: The adjusted closing price after stock splits and dividends
      • Returns: Daily percentage return based on close prices
      • Volume: The volume of shares traded that day

    3. NASDAQ

    This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

    • Columns:
      • Date: The date of the data point
      • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
      • Open: The opening price of the index
      • High: The highest value reached on that day
      • Low: The lowest value reached on that day
      • Close: The closing value of the index
      • Adjusted: The adjusted closing value after any corporate actions
      • Returns: Daily percentage return based on close values
      • Volume: The volume of shares traded

    4. S&P 500

    This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

    • Columns:
      • Date: The date of the data point
      • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
      • Open: The opening price of the index
      • High: The highest value reached on that day
      • Low: The lowest value reached on that day
      • Close: The closing value of the index
      • Adjusted: The adjusted closing value after any corporate actions
      • Returns: Daily percentage return based on close values
      • Volume: The volume of shares traded

    5. Dow Jones

    This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

    • Columns:
      • Date: The date of the data point
      • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
      • Open: The opening price of the index
      • High: The highest value reached on that day
      • Low: The lowest value reached on that day
      • Close: The closing value of the index
      • Adjusted: The adjusted closing value after any corporate actions
      • Returns: Daily percentage return based on close values
      • Volume: The volume of shares traded

    Personal Portfolio Data

    This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

    This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

  4. EOD data for all Dow Jones stocks

    • kaggle.com
    zip
    Updated Jun 12, 2019
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Timo Bozsolik (2019). EOD data for all Dow Jones stocks [Dataset]. https://www.kaggle.com/datasets/timoboz/stock-data-dow-jones
    Explore at:
    zip(1697460 bytes)Available download formats
    Dataset updated
    Jun 12, 2019
    Authors
    Timo Bozsolik
    Description

    Update

    Unfortunately, the API this dataset used to pull the stock data isn't free anymore. Instead of having this auto-updating, I dropped the last version of the data files in here, so at least the historic data is still usable.

    Content

    This dataset provides free end of day data for all stocks currently in the Dow Jones Industrial Average. For each of the 30 components of the index, there is one CSV file named by the stock's symbol (e.g. AAPL for Apple). Each file provides historically adjusted market-wide data (daily, max. 5 years back). See here for description of the columns: https://iextrading.com/developer/docs/#chart

    Since this dataset uses remote URLs as files, it is automatically updated daily by the Kaggle platform and automatically represents the latest data.

    Acknowledgements

    List of stocks and symbols as per https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average

    Thanks to https://iextrading.com for providing this data for free!

    Terms of Use

    Data provided for free by IEX. View IEX’s Terms of Use.

  5. F

    Dow Jones Industrial Average

    • fred.stlouisfed.org
    json
    Updated Jun 20, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2025). Dow Jones Industrial Average [Dataset]. https://fred.stlouisfed.org/series/DJIA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 20, 2025
    License

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

    Description

    Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-06-22 to 2025-06-20 about stock market, average, industry, and USA.

  6. F

    S&P 500

    • fred.stlouisfed.org
    json
    Updated Jun 20, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 20, 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.

  7. k

    The Dow Jones Industrial Average (Forecast)

    • kappasignal.com
    Updated Mar 29, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2023). The Dow Jones Industrial Average (Forecast) [Dataset]. https://www.kappasignal.com/2023/03/the-dow-jones-industrial-average.html
    Explore at:
    Dataset updated
    Mar 29, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    The Dow Jones Industrial Average

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  8. k

    Will the Dow Jones Industrial Average Index Rise Today? (Forecast)

    • kappasignal.com
    Updated Aug 10, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2024). Will the Dow Jones Industrial Average Index Rise Today? (Forecast) [Dataset]. https://www.kappasignal.com/2024/08/will-dow-jones-industrial-average-index_10.html
    Explore at:
    Dataset updated
    Aug 10, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Will the Dow Jones Industrial Average Index Rise Today?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  9. k

    Dow Jones Industrial Average Index Target Price Prediction (Forecast)

    • kappasignal.com
    Updated Oct 25, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2022). Dow Jones Industrial Average Index Target Price Prediction (Forecast) [Dataset]. https://www.kappasignal.com/2022/10/dow-jones-industrial-average-index_25.html
    Explore at:
    Dataset updated
    Oct 25, 2022
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Dow Jones Industrial Average Index Target Price Prediction

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  10. Stock market statistics, Canada and United States, Bank of Canada

    • open.canada.ca
    • www150.statcan.gc.ca
    • +3more
    csv, html, xml
    Updated Jan 17, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statistics Canada (2023). Stock market statistics, Canada and United States, Bank of Canada [Dataset]. https://open.canada.ca/data/en/dataset/e037b4dd-4c13-4cc2-b8c4-0262083dbbd0
    Explore at:
    csv, xml, htmlAvailable download formats
    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Area covered
    United States, Canada
    Description

    This table contains 14 series, with data starting from 1953 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Stock market statistics (14 items: Toronto Stock Exchange; value of shares traded; United States common stocks; Dow-Jones industrials; high; United States common stocks; Dow-Jones industrials; low; Toronto Stock Exchange; volume of shares traded ...).

  11. k

    Dow Jones U.S. Select Insurance Index: Poised for a Rebound? (Forecast)

    • kappasignal.com
    Updated Apr 25, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2024). Dow Jones U.S. Select Insurance Index: Poised for a Rebound? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/dow-jones-us-select-insurance-index.html
    Explore at:
    Dataset updated
    Apr 25, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Dow Jones U.S. Select Insurance Index: Poised for a Rebound?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  12. J

    The CAPM with Measurement Error: "There's life in the old dog yet!"...

    • journaldata.zbw.eu
    .dat, .fmt, .gss +8
    Updated Mar 4, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Winfried Pohlmeier; Anastasia Simmet; Winfried Pohlmeier; Anastasia Simmet (2021). The CAPM with Measurement Error: "There's life in the old dog yet!" Replication data [Dataset]. http://doi.org/10.15456/jbnst.2019064.103528
    Explore at:
    .out, .fmt, application/vnd.wolfram.mathematica.package, .dat, txt, .gss, pdf, pdb, .inc, csv, .matAvailable download formats
    Dataset updated
    Mar 4, 2021
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Winfried Pohlmeier; Anastasia Simmet; Winfried Pohlmeier; Anastasia Simmet
    License

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

    Description

    The replication data contain MATLAB and GAUSS codes as well as the data required for replication of the results from the paper

    1. Monte Carlo Simulation:

    Contains codes and data for simulation study from Section 3.

    Data:

    • MV.mat, MV.txt- monthly data on market capitalization of the 205 stocks of the S&P500 index obtained from DataStream for the period 01.01.1974-01.05.2015

    • sp500_edata.mat - monthly data on close prices of components of S&P500 index for the period 01.01.1974-01.05.2015 processed to obtain excess returns using as a risk free return data on the risk free return from French & Fama database. Description of the price data from DataStream: "The ‘current’ prices taken at the close of market are stored each day. These stored prices are adjusted for subsequent capital actions, and this adjusted figure then becomes the default price offered on all Research programs. " Description of the excess return of the market from French & Fama database : "the excess return on the market, value-weight return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ that have a CRSP share code of 10 or 11 at the beginning of month t, good shares and price data at the beginning of t, and good return data for t minus theone-month Treasury bill rate (from Ibbotson Associates)." From the latest file two separate data files were created (see CAPMsim.m):

    • sp500_stocks.txt, sp500_stocks.mat - monthly data on close prices of 205 components of S&P500 index for the period 01.01.1974-01.05.2015

    • FactorData.txt, FactorData.txt - The Fama & French factors from French & Fama database for a period July 1926 - May 2015.

    Codes:

    • CAPMsim.m - the main code that replicates the Monte Carlo simulation of the artificial market and proxy indexes subject to different types of the measurement error.

    • sure.m- obtains the estimated parameters for the SUR system and performs hypothesis testing of the significance of the coefficients.

    2. Empirical Application

    Contains codes and data for empirical application from Section 4.

    Data:

    • data1203.txt - 120 monthly observations on the excess returns on 20 random stocks from S&P500, S&P500 index return, DJIA return from DataStream and excess return of the CRSP index from French & Fama database for a period 01/06/2005-01/05/2015.
    • data1204.txt - 120 monthly observations on the excess returns on 30 stocks from DJIA, S&P500 index return, DJIA return from DataStream and excess return of the CRSP index from French & Fama database for a period 01/06/2005-01/05/2015.

    • DJSTOCKS_60_FF_Z.dat - 60 monthly observations on the excess returns on 30 stocks from DJIA from DataStream and excess return of the CRSP index from French & Fama database for a period 01/06/2010-01/05/2015.

    • DJSTOCKS_60_SP_Z.dat - 60 monthly observations on the excess returns on 30 stocks from DJIA and S&P500 index return from DataStream for a period 01/06/2010-01/05/2015.

      • DJSTOCKS_60_DJ_Z.dat - 60 monthly observations on the excess returns on 30 stocks from DJIA and DJIA return from DataStream for a period 01/06/2010-01/05/2015.
      • STOCKS_60_FF_Z.dat - 60 monthly observations on the excess returns on 20 random stocks from S&P500 from DataStream and excess return of the CRSP index from French & Fama database for a period 01/06/2010-01/05/2015.
      • STOCKS_60_SP_Z.dat - 60 monthly observations on the excess returns on 20 random stocks from S&P500 and S&P500 index return from DataStream for a period 01/06/2010-01/05/2015.
    • STOCKS_60_DJ_Z.dat - 60 monthly observations on the excess returns on 20 random stocks from S&P500 and DJIA return from DataStream for a period 01/06/2010-01/05/2015.

      Description of the variables in the data sets:

    • Z_1, Z_2,...,Z_20,..., Z_30 - returns of individual stocks depending on the data set.

    • For calculation of the returns adjusted prices from DataStream were used (see data from Monte Carlo simulation part). Risk free return is taken from French & Fama database.

    • Time period was shortened from 120 to 60 observations: 01/06/2010-01/05/2015

    • Excess returns from the market and indeces:

      • Z_SP - 60 observations on excess return of the S&P500 from DataStream
      • Z_DJ - 60 observations on excess return of the DJIA from DataStream
      • Z_FF - 60 observations on excess return of the market from French & Fama database

    Codes:

    • load_stocks120.gss - loads the data on the returns of the randomly selected 20 socks of S&P500 and selects last 60 observations
      • load_djstocks120.gss - loads the data on the returns of the 30 socks of the Dow-Jones Industrial Average Index and selects last 60 observations
      • CAPM.prc- contains functions to estimate CAPM model by SUR and Minimum Distance methods
    • CAPM.inc- sets the format for the output files from the GAUSS procedures
    • CAPM_STOCKS20_FF.gss, CAPM_STOCKS20_DJ.gss, CAPM_STOCKS20_SP.gss, CAPM_DJSTOCKS30_FF.gss,CAPM_DJSTOCKS30_DJ.gss,CAPM_DJSTOCKS30_SP.gss - GAUSS procedures to estimate the CAPM models based on particular data set (20 random stocks or 30 stocks from DJIA as well as different market indexes: S&P500, DJIA, CRSP) and generate separate output files. 2019-03-05 11:51:42.893129 The replication data contain MATLAB and GAUSS codes as well as the data required for replication of the results from the paper
  13. k

    The Dow Jones U.S. Completion Total Stock Market Index (Forecast)

    • kappasignal.com
    Updated May 8, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2023). The Dow Jones U.S. Completion Total Stock Market Index (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/the-dow-jones-us-completion-total-stock.html
    Explore at:
    Dataset updated
    May 8, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    The Dow Jones U.S. Completion Total Stock Market Index

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  14. k

    Dow Jones Industrial Average Index assigned short-term B1 & long-term Ba1...

    • kappasignal.com
    Updated Oct 24, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2022). Dow Jones Industrial Average Index assigned short-term B1 & long-term Ba1 forecasted stock rating. (Forecast) [Dataset]. https://www.kappasignal.com/2022/10/dow-jones-industrial-average-index.html
    Explore at:
    Dataset updated
    Oct 24, 2022
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Dow Jones Industrial Average Index assigned short-term B1 & long-term Ba1 forecasted stock rating.

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  15. k

    Will the Dow Jones Industrial Average Index Maintain Its Momentum?...

    • kappasignal.com
    Updated Aug 27, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2024). Will the Dow Jones Industrial Average Index Maintain Its Momentum? (Forecast) [Dataset]. https://www.kappasignal.com/2024/08/will-dow-jones-industrial-average-index_27.html
    Explore at:
    Dataset updated
    Aug 27, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Will the Dow Jones Industrial Average Index Maintain Its Momentum?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  16. k

    Short/Long Term Stocks: Dow Jones New Zealand Index Stock Forecast...

    • kappasignal.com
    Updated Oct 21, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2022). Short/Long Term Stocks: Dow Jones New Zealand Index Stock Forecast (Forecast) [Dataset]. https://www.kappasignal.com/2022/10/shortlong-term-stocks-dow-jones-new.html
    Explore at:
    Dataset updated
    Oct 21, 2022
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Short/Long Term Stocks: Dow Jones New Zealand Index Stock Forecast

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  17. k

    Dow Jones U.S. Real Estate: A True Reflection of the Market? (Forecast)

    • kappasignal.com
    Updated Apr 20, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2024). Dow Jones U.S. Real Estate: A True Reflection of the Market? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/dow-jones-us-real-estate-true.html
    Explore at:
    Dataset updated
    Apr 20, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Dow Jones U.S. Real Estate: A True Reflection of the Market?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  18. k

    Surging Services: Will Dow Jones CPI Signal Continued Consumer Strength?...

    • kappasignal.com
    Updated Apr 28, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2024). Surging Services: Will Dow Jones CPI Signal Continued Consumer Strength? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/surging-services-will-dow-jones-cpi.html
    Explore at:
    Dataset updated
    Apr 28, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Surging Services: Will Dow Jones CPI Signal Continued Consumer Strength?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  19. k

    Will the Dow Jones U.S. Oil & Gas Index Fuel Further Gains? (Forecast)

    • kappasignal.com
    Updated Jun 18, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2025). Will the Dow Jones U.S. Oil & Gas Index Fuel Further Gains? (Forecast) [Dataset]. https://www.kappasignal.com/2024/11/will-dow-jones-us-oil-gas-index-fuel_13.html
    Explore at:
    Dataset updated
    Jun 18, 2025
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Will the Dow Jones U.S. Oil & Gas Index Fuel Further Gains?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  20. k

    Dow Jones U.S. Financial Services Index: Strength or Weakness Ahead?...

    • kappasignal.com
    Updated Mar 27, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    KappaSignal (2024). Dow Jones U.S. Financial Services Index: Strength or Weakness Ahead? (Forecast) [Dataset]. https://www.kappasignal.com/2024/03/dow-jones-us-financial-services-index.html
    Explore at:
    Dataset updated
    Mar 27, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    Dow Jones U.S. Financial Services Index: Strength or Weakness Ahead?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
MACROTRENDS (2025). Dow Jones - 10 Year Daily Chart [Dataset]. https://www.macrotrends.net/1358/dow-jones-industrial-average-last-10-years

Dow Jones - 10 Year Daily Chart

Dow Jones - 10 Year Daily Chart

Explore at:
15 scholarly articles cite this dataset (View in Google Scholar)
csvAvailable download formats
Dataset updated
Jun 30, 2025
Dataset authored and provided by
MACROTRENDS
License

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

Time period covered
1915 - 2025
Area covered
United States
Description

Interactive chart illustrating the performance of the Dow Jones Industrial Average (DJIA) market index over the last ten years. Each point of the stock market graph is represented by the daily closing price for the DJIA. Historical data can be downloaded via the red button on the upper left corner of the chart.

Search
Clear search
Close search
Google apps
Main menu