8 datasets found
  1. k

    IRD Stock: A Solid Investment (Forecast)

    • kappasignal.com
    Updated Aug 24, 2023
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    KappaSignal (2023). IRD Stock: A Solid Investment (Forecast) [Dataset]. https://www.kappasignal.com/2023/08/ird-stock-solid-investment.html
    Explore at:
    Dataset updated
    Aug 24, 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.

    IRD Stock: A Solid Investment

    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

  2. e

    Eximpedia Export Import Trade

    • eximpedia.app
    Updated Jan 26, 2025
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    Seair Exim (2025). Eximpedia Export Import Trade [Dataset]. https://www.eximpedia.app/
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Jan 26, 2025
    Dataset provided by
    Eximpedia Export Import Trade Data
    Eximpedia PTE LTD
    Authors
    Seair Exim
    Area covered
    Morocco, Cayman Islands, Saint Martin (French part), Åland Islands, Heard Island and McDonald Islands, Montserrat, Thailand, Syrian Arab Republic, Chad, Slovakia
    Description

    Ird Pat Traffic Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

  3. Opus Genetics (IRD) Stock Projected for Significant Growth Amidst Promising...

    • kappasignal.com
    Updated Apr 10, 2025
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    KappaSignal (2025). Opus Genetics (IRD) Stock Projected for Significant Growth Amidst Promising Pipeline (Forecast) [Dataset]. https://www.kappasignal.com/2025/04/opus-genetics-ird-stock-projected-for.html
    Explore at:
    Dataset updated
    Apr 10, 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.

    Opus Genetics (IRD) Stock Projected for Significant Growth Amidst Promising Pipeline

    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. e

    Eximpedia Export Import Trade

    • eximpedia.app
    Updated Jan 9, 2025
    + more versions
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    Seair Exim (2025). Eximpedia Export Import Trade [Dataset]. https://www.eximpedia.app/
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Jan 9, 2025
    Dataset provided by
    Eximpedia Export Import Trade Data
    Eximpedia PTE LTD
    Authors
    Seair Exim
    Area covered
    Burundi, French Polynesia, Azerbaijan, Saint Barthélemy, Jersey, Vietnam, Nigeria, Peru, Micronesia (Federated States of), Japan
    Description

    Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

  5. BGC Partners MiFID II MTF and OTF

    • eulerpool.com
    Updated Jul 3, 2025
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    Eulerpool (2025). BGC Partners MiFID II MTF and OTF [Dataset]. https://eulerpool.com/en/data-analytics/financial-data/pricing-and-market-data/bgc-partners-mifid-ii-mtf-and-otf
    Explore at:
    Dataset updated
    Jul 3, 2025
    Dataset provided by
    Eulerpool Research Systems
    Authors
    Eulerpool
    Description

    BGC Partners functions as both an Organised Trading Facility (OTF) and a Multilateral Trading Facility (MTF) under MiFID II, providing services for pre-trade and post-trade as well as transaction reporting. Compliance with MiFID II regulations is supported by publishing BGC OTF and MTF data for customer access. About BGC Partners: BGC Partners stands as a premier global brokerage firm catering to the financial and real estate sectors. With headquarters in London and New York, BGC Partners offers a broad spectrum of services such as trade execution, broker-dealer operations, clearing, processing, information dissemination, and additional back office solutions.

  6. R D International Importer and Shaoxing Broadtex Trade Co Limited Exporter...

    • seair.co.in
    Updated Feb 18, 2024
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    Seair Exim (2024). R D International Importer and Shaoxing Broadtex Trade Co Limited Exporter Data to USA [Dataset]. https://www.seair.co.in
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 18, 2024
    Dataset provided by
    Seair Exim Solutions
    Authors
    Seair Exim
    Area covered
    United States
    Description

    Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

  7. R D International Importer and Wuxi Jht Trade Co Limited Exporter Data to...

    • seair.co.in
    Updated Feb 18, 2024
    Share
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    Seair Exim (2024). R D International Importer and Wuxi Jht Trade Co Limited Exporter Data to USA [Dataset]. https://www.seair.co.in
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 18, 2024
    Dataset provided by
    Seair Exim Solutions
    Authors
    Seair Exim
    Area covered
    United States
    Description

    Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

  8. R D International Importer and Suzhou Yuewen Textile Trade Co Limited...

    • seair.co.in
    Updated Feb 18, 2024
    Share
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    Email
    Click to copy link
    Link copied
    Close
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    Seair Exim (2024). R D International Importer and Suzhou Yuewen Textile Trade Co Limited Exporter Data to USA [Dataset]. https://www.seair.co.in
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 18, 2024
    Dataset provided by
    Seair Exim Solutions
    Authors
    Seair Exim
    Area covered
    United States
    Description

    Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
KappaSignal (2023). IRD Stock: A Solid Investment (Forecast) [Dataset]. https://www.kappasignal.com/2023/08/ird-stock-solid-investment.html

IRD Stock: A Solid Investment (Forecast)

Explore at:
Dataset updated
Aug 24, 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.

IRD Stock: A Solid Investment

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

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