13 datasets found
  1. S

    Beef Cattle Stock Market

    • indexbox.io
    doc, docx, pdf, xls +1
    Updated Jun 1, 2025
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    IndexBox Inc. (2025). Beef Cattle Stock Market [Dataset]. https://www.indexbox.io/search/beef-cattle-stock-market/
    Explore at:
    xlsx, doc, xls, pdf, docxAvailable download formats
    Dataset updated
    Jun 1, 2025
    Dataset authored and provided by
    IndexBox Inc.
    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, 2012 - Jun 25, 2025
    Area covered
    World
    Variables measured
    Price CIF, Price FOB, Export Value, Import Price, Import Value, Export Prices, Export Volume, Import Volume
    Description

    The beef cattle industry is driven by factors such as demand for meat, changes in consumer preferences, growth in population, and economic conditions. This article discusses how these factors influence the beef cattle stock market and emphasizes the importance of understanding the different segments of the industry for producers, buyers, and investors.

  2. T

    Live Cattle - Price Data

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 2, 2025
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    TRADING ECONOMICS (2025). Live Cattle - Price Data [Dataset]. https://tradingeconomics.com/commodity/live-cattle
    Explore at:
    csv, excel, json, xmlAvailable download formats
    Dataset updated
    Jul 2, 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 2, 1980 - Jul 1, 2025
    Area covered
    World
    Description

    Live Cattle fell to 210.76 USd/Lbs on July 1, 2025, down 1.51% from the previous day. Over the past month, Live Cattle's price has fallen 2.63%, but it is still 13.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Live Cattle - values, historical data, forecasts and news - updated on July of 2025.

  3. CME feeder cattle futures (Forecast)

    • kappasignal.com
    Updated May 9, 2023
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    KappaSignal (2023). CME feeder cattle futures (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/cme-feeder-cattle-futures.html
    Explore at:
    Dataset updated
    May 9, 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.

    CME feeder cattle futures

    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

  4. k

    Zoetis' (ZTS) Livestock-Focused Strategy Expected to Yield Positive Results,...

    • kappasignal.com
    Updated Apr 30, 2025
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    KappaSignal (2025). Zoetis' (ZTS) Livestock-Focused Strategy Expected to Yield Positive Results, Analysts Predict. (Forecast) [Dataset]. https://www.kappasignal.com/2025/04/zoetis-zts-livestock-focused-strategy.html
    Explore at:
    Dataset updated
    Apr 30, 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.

    Zoetis' (ZTS) Livestock-Focused Strategy Expected to Yield Positive Results, Analysts Predict.

    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

  5. Dataset: Pacer US Small Cap Cash Cows Growth Leaders ETF (CAFG) Stock...

    • zenodo.org
    csv
    Updated Jun 26, 2024
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    Nitiraj Kulkarni; Nitiraj Kulkarni; Jagadish Tawade; Jagadish Tawade (2024). Dataset: Pacer US Small Cap Cash Cows Growth Leaders ETF (CAFG) Stock Performance [Dataset]. http://doi.org/10.5281/zenodo.12554363
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 26, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Nitiraj Kulkarni; Nitiraj Kulkarni; Jagadish Tawade; Jagadish Tawade
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

  6. k

    Lean Hogs: TR/CC CRB Forecasts Continued Rise for the Livestock Index...

    • kappasignal.com
    Updated Apr 26, 2025
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    KappaSignal (2025). Lean Hogs: TR/CC CRB Forecasts Continued Rise for the Livestock Index (Forecast) [Dataset]. https://www.kappasignal.com/2025/04/lean-hogs-trcc-crb-forecasts-continued.html
    Explore at:
    Dataset updated
    Apr 26, 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.

    Lean Hogs: TR/CC CRB Forecasts Continued Rise for the Livestock 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

  7. f

    Daily log-returns of the Nikkei Index larger than 5% for the past decade.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Jun-ichi Maskawa (2023). Daily log-returns of the Nikkei Index larger than 5% for the past decade. [Dataset]. http://doi.org/10.1371/journal.pone.0160152.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Jun-ichi Maskawa
    License

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

    Description

    Daily log-returns of the Nikkei Index larger than 5% for the past decade.

  8. f

    Daily log-returns of the Nikkei Index less than -5% during the past decade.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Jun-ichi Maskawa (2023). Daily log-returns of the Nikkei Index less than -5% during the past decade. [Dataset]. http://doi.org/10.1371/journal.pone.0160152.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Jun-ichi Maskawa
    License

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

    Description

    Daily log-returns of the Nikkei Index less than -5% during the past decade.

  9. k

    Elanco Animal Health (ELAN) Stock: A Pawsitive Outlook for the Future?...

    • kappasignal.com
    Updated Oct 1, 2024
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    KappaSignal (2024). Elanco Animal Health (ELAN) Stock: A Pawsitive Outlook for the Future? (Forecast) [Dataset]. https://www.kappasignal.com/2024/10/elanco-animal-health-elan-stock.html
    Explore at:
    Dataset updated
    Oct 1, 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.

    Elanco Animal Health (ELAN) Stock: A Pawsitive Outlook for the Future?

    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. i

    Zoetis Exceeds Revenue Forecasts: Investors Anticipate Upcoming Report -...

    • indexbox.io
    doc, docx, pdf, xls +1
    Updated Jun 1, 2025
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    IndexBox Inc. (2025). Zoetis Exceeds Revenue Forecasts: Investors Anticipate Upcoming Report - News and Statistics - IndexBox [Dataset]. https://www.indexbox.io/blog/zoetis-surpasses-revenue-expectations-and-eyes-future-growth/
    Explore at:
    doc, pdf, xls, xlsx, docxAvailable download formats
    Dataset updated
    Jun 1, 2025
    Dataset authored and provided by
    IndexBox Inc.
    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, 2012 - Jun 1, 2025
    Area covered
    World
    Variables measured
    Market Size, Market Share, Tariff Rates, Average Price, Export Volume, Import Volume, Demand Elasticity, Market Growth Rate, Market Segmentation, Volume of Production, and 4 more
    Description

    Zoetis exceeded past revenue forecasts with an 11% surge, and investors await the next report, hoping for continued growth and financial performance improvements.

  11. T

    Beef - Price Data

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Mar 15, 2013
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    TRADING ECONOMICS (2013). Beef - Price Data [Dataset]. https://tradingeconomics.com/commodity/beef
    Explore at:
    json, excel, xml, csvAvailable download formats
    Dataset updated
    Mar 15, 2013
    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 25, 2001 - Jul 1, 2025
    Area covered
    World
    Description

    Beef fell to 310.65 BRL/15KG on July 1, 2025, down 2.13% from the previous day. Over the past month, Beef's price has risen 1.49%, and is up 37.88% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Beef - values, historical data, forecasts and news - updated on July of 2025.

  12. k

    Elanco Earnings: Animal Health Sector Resilient Amidst Economic Headwinds?...

    • kappasignal.com
    Updated Feb 9, 2024
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    KappaSignal (2024). Elanco Earnings: Animal Health Sector Resilient Amidst Economic Headwinds? (ELAN) (Forecast) [Dataset]. https://www.kappasignal.com/2024/02/elanco-earnings-animal-health-sector.html
    Explore at:
    Dataset updated
    Feb 9, 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.

    Elanco Earnings: Animal Health Sector Resilient Amidst Economic Headwinds? (ELAN)

    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

  13. k

    Phibro Animal Health Corp (PAHC) Stock: New Heights or Plateau Ahead?...

    • kappasignal.com
    Updated Mar 22, 2024
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    KappaSignal (2024). Phibro Animal Health Corp (PAHC) Stock: New Heights or Plateau Ahead? (Forecast) [Dataset]. https://www.kappasignal.com/2024/03/phibro-animal-health-corp-pahc-stock.html
    Explore at:
    Dataset updated
    Mar 22, 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.

    Phibro Animal Health Corp (PAHC) Stock: New Heights or Plateau 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

  14. 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
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IndexBox Inc. (2025). Beef Cattle Stock Market [Dataset]. https://www.indexbox.io/search/beef-cattle-stock-market/

Beef Cattle Stock Market

Explore at:
xlsx, doc, xls, pdf, docxAvailable download formats
Dataset updated
Jun 1, 2025
Dataset authored and provided by
IndexBox Inc.
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, 2012 - Jun 25, 2025
Area covered
World
Variables measured
Price CIF, Price FOB, Export Value, Import Price, Import Value, Export Prices, Export Volume, Import Volume
Description

The beef cattle industry is driven by factors such as demand for meat, changes in consumer preferences, growth in population, and economic conditions. This article discusses how these factors influence the beef cattle stock market and emphasizes the importance of understanding the different segments of the industry for producers, buyers, and investors.

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