4 datasets found
  1. Financial data of Tesla, Apple and Microsoft

    • kaggle.com
    Updated May 5, 2025
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    Yesha Vyas (2025). Financial data of Tesla, Apple and Microsoft [Dataset]. https://www.kaggle.com/datasets/yeshavyas/financial-data-of-tesla-apple-and-microsoft
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 5, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yesha Vyas
    License

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

    Description

    Includes the net income, net revenue, total assets, total liabilities, and cash flow of operations for years 2022, 2023, and 2024 extracted from 10k filings of the respective organizations.

  2. Apple Security Market Data

    • kaggle.com
    Updated Sep 6, 2023
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    Sanket2002 (2023). Apple Security Market Data [Dataset]. https://www.kaggle.com/datasets/sanket2002/apple-security-market-data/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 6, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sanket2002
    Description

    The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

    To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

    To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

  3. Top Tech Companies Stock Price

    • kaggle.com
    Updated Nov 24, 2020
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    Tomas Mantero (2020). Top Tech Companies Stock Price [Dataset]. https://www.kaggle.com/datasets/tomasmantero/top-tech-companies-stock-price
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 24, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Tomas Mantero
    License

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

    Description

    Context

    In this dataset you can find the Top 100 companies in the technology sector. You can also find 5 of the most important and used indices in the financial market as well as a list of all the companies in the S&P 500 index and in the technology sector.

    The Global Industry Classification Standard also known as GICS is the primary financial industry standard for defining sector classifications. The Global Industry Classification Standard was developed by index providers MSCI and Standard and Poor’s. Its hierarchy begins with 11 sectors which can be further delineated to 24 industry groups, 69 industries, and 158 sub-industries.

    You can read the definition of each sector here.

    The 11 broad GICS sectors commonly used for sector breakdown reporting include the following: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, Utilities and Real Estate.

    In this case we will focuse in the Technology Sector. You can see all the sectors and industry groups here.

    To determine which companies, correspond to the technology sector, we use Yahoo Finance, where we rank the companies according to their “Market Cap”. After having the list of the Top 100 best valued companies in the sector, we proceeded to download the historical data of each of the companies using the NASDAQ website.

    Regarding to the indices, we searched various sources to find out which were the most used and determined that the 5 most frequently used indices are: Dow Jones Industrial Average (DJI), S&P 500 (SPX), NASDAQ Composite (IXIC), Wilshire 5000 Total Market Inde (W5000) and to specifically view the technology sector SPDR Select Sector Fund - Technology (XLK). Historical data for these indices was also obtained from the NASDQ website.

    Content

    In total there are 107 files in csv format. They are composed as follows:

    • 100 files contain the historical data of tech companies.
    • 5 files contain the historical data of the most used indices.
    • 1 file contain the list of all the companies in the S&P 500 index.
    • 1 file contain the list of all the companies in the technology sector.

    Column Description

    Every company and index file has the same structure with the same columns:

    Date: It is the date on which the prices were recorded. High: Is the highest price at which a stock traded during the course of the trading day. Low: Is the lowest price at which a stock traded during the course of the trading day. Open: Is the price at which a stock started trading when the opening bell rang. Close: Is the last price at which a stock trades during a regular trading session. Volume: Is the number of shares that changed hands during a given day. Adj Close: The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

    The two other files have different columns names:

    List of S&P 500 companies

    Symbol: Ticker symbol of the company. Name: Name of the company. Sector: The sector to which the company belongs.

    Technology Sector Companies List

    Symbol: Ticker symbol of the company. Name: Name of the company. Price: Current price at which a stock can be purchased or sold. (11/24/20) Change: Net change is the difference between closing prices from one day to the next. % Change: Is the difference between closing prices from one day to the next in percentage. Volume: Is the number of shares that changed hands during a given day. Avg Vol: Is the daily average of the cumulative trading volume during the last three months. Market Cap (Billions): Is the total value of a company’s shares outstanding at a given moment in time. It is calculated by multiplying the number of shares outstanding by the price of a single share. PE Ratio: Is the ratio of a company's share (stock) price to the company's earnings per share. The ratio is used for valuing companies and to find out whether they are overvalued or undervalued.

    Acknowledgements

    SEC EDGAR | Company Filings NASDAQ | Historical Quotes Yahoo Finance | Technology Sector Wikipedia | List of S&P 500 companies S&P Dow Jones Indices | S&P 500 [S&P Dow Jones Indices | DJI](https://www.spglobal.com/spdji/en/i...

  4. EOD data for all Dow Jones stocks

    • kaggle.com
    zip
    Updated Jun 12, 2019
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    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. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Yesha Vyas (2025). Financial data of Tesla, Apple and Microsoft [Dataset]. https://www.kaggle.com/datasets/yeshavyas/financial-data-of-tesla-apple-and-microsoft
Organization logo

Financial data of Tesla, Apple and Microsoft

Includes 2024,2023, and 2022 financial data from 10K filings.

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
May 5, 2025
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Yesha Vyas
License

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

Description

Includes the net income, net revenue, total assets, total liabilities, and cash flow of operations for years 2022, 2023, and 2024 extracted from 10k filings of the respective organizations.

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