5 datasets found
  1. T

    Apple | AAPL - PE Price to Earnings

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Sep 16, 2025
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    TRADING ECONOMICS (2025). Apple | AAPL - PE Price to Earnings [Dataset]. https://tradingeconomics.com/aapl:us:pe
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    Sep 16, 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 1, 2000 - Dec 2, 2025
    Area covered
    United States
    Description

    Apple reported $34.22 in PE Price to Earnings for its fiscal quarter ending in September of 2025. Data for Apple | AAPL - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last December in 2025.

  2. Apple Inc. (NASDAQ: AAPL) (Forecast)

    • kappasignal.com
    Updated May 18, 2023
    + more versions
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    KappaSignal (2023). Apple Inc. (NASDAQ: AAPL) (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/apple-inc-nasdaq-aapl.html
    Explore at:
    Dataset updated
    May 18, 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.

    Apple Inc. (NASDAQ: AAPL)

    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

  3. Apple Stock Data (1999-2024)

    • kaggle.com
    zip
    Updated Jul 14, 2024
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    Ayesha Aqib (2024). Apple Stock Data (1999-2024) [Dataset]. https://www.kaggle.com/datasets/ayeshaaqib/apple-stock-data-1999-2024/code
    Explore at:
    zip(110132 bytes)Available download formats
    Dataset updated
    Jul 14, 2024
    Authors
    Ayesha Aqib
    License

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

    Description

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16770905%2Fc1278d8fb31c245690414c402b69c308%2F2.jpg?generation=1723295840035480&alt=media" alt="">

    Description:

    This dataset contains daily stock price data for Apple Inc. (AAPL) from 1999 to 2024. It includes open, high, low, close prices, and trading volume, supplemented with fundamental analysis metrics such as P/E ratio, EPS, and market capitalization. This comprehensive dataset is ideal for financial analysis, time series forecasting, and exploring correlations between stock performance and fundamental indicators.

    Key Features

    • Historical daily stock prices (Open, High, Low, Close).
    • Trading volume data.
    • Fundamental metrics: P/E ratio, EPS, Market Capitalization.
    • Useful for stock market analysis, predictive modeling, and machine learning applications.

    Dataset Source

    • Alpha Vantage API

    License

    • Creative Commons Attribution 4.0 International (CC BY 4.0)
  4. Stock Dataset

    • kaggle.com
    zip
    Updated Dec 5, 2024
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    aqsa umar (2024). Stock Dataset [Dataset]. https://www.kaggle.com/datasets/aqsaumar/stock-dataset/data
    Explore at:
    zip(148632 bytes)Available download formats
    Dataset updated
    Dec 5, 2024
    Authors
    aqsa umar
    License

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

    Description

    Stock Market Dataset Columns** The dataset generated using the yfinance library typically contains two types of data: - Historical Stock Prices - Company Metadata

    A. Historical Stock Prices

    This data provides a time series of a stock's market performance. Below are the main columns and their explanations:

    ColumnDescription
    DateThe date for the recorded stock data.
    OpenThe price at which the stock started trading on that day.
    HighThe highest price reached during that day.
    LowThe lowest price reached during that day.
    CloseThe price at which the stock closed trading on that day.
    Adj CloseThe adjusted closing price accounting for corporate actions like stock splits and dividends.
    VolumeThe total number of shares traded on that day.

    Example:

    DateOpenHighLowCloseAdj CloseVolume
    2022-01-03170.0172.5169.2172.0171.21200000

    B. Company Metadata

    This data provides descriptive information about the company associated with the stock. Columns and their meanings include:

    ColumnDescription
    TickerThe stock ticker symbol (e.g., AAPL for Apple Inc.).
    CompanyThe full name of the company (e.g., Apple Inc.).
    SectorThe industry sector to which the company belongs (e.g., Technology).
    IndustryThe specific industry within the sector (e.g., Consumer Electronics).
    Market CapThe total market value of the company’s outstanding shares in USD.
    P/E RatioThe company's Price-to-Earnings ratio, indicating how expensive the stock is relative to its earnings.

    Example:

    TickerCompanySectorIndustryMarket CapP/E Ratio
    AAPLApple Inc.TechnologyConsumer Hardware$2.5 Trillion28.3
  5. Top Tech Companies Stock Price

    • kaggle.com
    zip
    Updated Nov 24, 2020
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    Tomas Mantero (2020). Top Tech Companies Stock Price [Dataset]. https://www.kaggle.com/tomasmantero/top-tech-companies-stock-price
    Explore at:
    zip(7295960 bytes)Available download formats
    Dataset updated
    Nov 24, 2020
    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...

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Share
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TRADING ECONOMICS (2025). Apple | AAPL - PE Price to Earnings [Dataset]. https://tradingeconomics.com/aapl:us:pe

Apple | AAPL - PE Price to Earnings

Explore at:
excel, xml, json, csvAvailable download formats
Dataset updated
Sep 16, 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 1, 2000 - Dec 2, 2025
Area covered
United States
Description

Apple reported $34.22 in PE Price to Earnings for its fiscal quarter ending in September of 2025. Data for Apple | AAPL - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last December in 2025.

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