23 datasets found
  1. Dataset: Vision Sensing Acquisition Corp. (VSACW) Stock Performance

    • zenodo.org
    csv
    Updated Jun 27, 2024
    + more versions
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    Nitiraj Kulkarni; Nitiraj Kulkarni; Jagadish Tawade; Jagadish Tawade (2024). Dataset: Vision Sensing Acquisition Corp. (VSACW) Stock Performance [Dataset]. http://doi.org/10.5281/zenodo.12566281
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 27, 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.

  2. Oil Stock Market Chart

    • indexbox.io
    doc, docx, pdf, xls +1
    Updated Jul 1, 2025
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    IndexBox Inc. (2025). Oil Stock Market Chart [Dataset]. https://www.indexbox.io/search/oil-stock-market-chart/
    Explore at:
    doc, pdf, docx, xls, xlsxAvailable download formats
    Dataset updated
    Jul 1, 2025
    Dataset provided by
    IndexBox
    Authors
    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 - Jul 10, 2025
    Area covered
    World
    Variables measured
    Price CIF, Price FOB, Export Value, Import Price, Import Value, Export Prices, Export Volume, Import Volume
    Description

    The oil stock market chart provides a visual representation of the performance and trends in the oil stock market. It helps investors analyze the performance of the oil sector, identify patterns, and make informed investment decisions. Learn more about how oil stock market charts work and their customization options.

  3. Card Stock Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 22, 2024
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    Dataintelo (2024). Card Stock Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-card-stock-market
    Explore at:
    pptx, pdf, csvAvailable download formats
    Dataset updated
    Sep 22, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Card Stock Market Outlook



    The global card stock market size was valued at approximately USD 2.8 billion in 2023 and is projected to grow to USD 4.2 billion by 2032, at a compound annual growth rate (CAGR) of 4.6% during the forecast period. This robust growth is driven by increasing demand in the packaging and printing industries, along with a burgeoning interest in crafting and DIY activities globally.



    One of the primary growth factors fueling the card stock market is the rising demand for sustainable and eco-friendly packaging solutions. As consumers and businesses alike become more environmentally conscious, the demand for recyclable and biodegradable card stock has surged. This trend is particularly evident in the packaging sector, where companies are increasingly opting for card stock over plastic to meet consumer preferences and regulatory requirements aimed at reducing plastic waste.



    The growth of the e-commerce industry is another significant driver for the card stock market. With the rapid expansion of online retailing, the need for secure and appealing packaging solutions has increased. Card stock is often used in packaging for its durability and printability, which helps in creating visually attractive and sturdy packaging. Moreover, the rise in personalized and custom packaging trends among e-commerce platforms has further amplified the demand for high-quality card stock.



    Additionally, the increasing popularity of crafting and DIY activities has spurred the demand for various types of card stock. With more people engaging in hobbies such as scrapbooking, card-making, and other creative projects, the market for card stock has expanded significantly. This trend is further bolstered by the proliferation of social media platforms, where users share their crafting ideas and projects, thereby inspiring others and driving demand for crafting materials, including card stock.



    From a regional perspective, North America and Europe hold significant shares in the card stock market, driven by high levels of consumer awareness and stringent environmental regulations. Asia Pacific, however, is expected to witness the fastest growth during the forecast period due to increasing industrialization, rising disposable income, and the growing e-commerce sector. Latin America and the Middle East & Africa are also anticipated to exhibit moderate growth, supported by expanding packaging and printing industries in these regions.



    Product Type Analysis



    The card stock market can be segmented by product type into coated card stock, uncoated card stock, textured card stock, recycled card stock, and others. Coated card stock holds a significant share due to its smooth surface and excellent printability, which makes it ideal for high-quality printing applications. It is widely used in business cards, brochures, and luxury packaging, where visual appeal is paramount. The coating enhances the card's durability and resistance to moisture, making it suitable for various commercial uses.



    Uncoated card stock, on the other hand, is preferred for applications that require a more natural and tactile feel. It is often used in stationery, greeting cards, and certain types of packaging where a rustic or minimalist aesthetic is desired. The lack of coating allows for better ink absorption, which can be advantageous for certain printing techniques and crafting projects.



    Textured card stock offers a unique advantage with its distinct surface patterns, adding a tactile dimension to printed materials. This type of card stock is popular in high-end invitations, business cards, and special event stationery. The textured surface can range from subtle linen-like patterns to more pronounced embossing, catering to diverse design needs.



    Recycled card stock is gaining traction due to the growing emphasis on sustainability. Made from post-consumer waste, this type of card stock appeals to eco-conscious consumers and businesses. It is used in a variety of applications, including packaging, printing, and crafting, and offers a viable alternative to traditional paper products with a lower environmental footprint.



    Other types of card stock include specialty variants tailored for specific applications, such as metallic finishes, which are used for luxury packaging and special occasions. These niche products, while not as widely used as the more common types, play an important role in meeting the diverse needs of the market and offering unique solutions for specific projects.

  4. Mobileye's Vision: Navigating the Future of (MBLY) (Forecast)

    • kappasignal.com
    Updated Oct 11, 2024
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    KappaSignal (2024). Mobileye's Vision: Navigating the Future of (MBLY) (Forecast) [Dataset]. https://www.kappasignal.com/2024/10/mobileyes-vision-navigating-future-of.html
    Explore at:
    Dataset updated
    Oct 11, 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.

    Mobileye's Vision: Navigating the Future of (MBLY)

    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: Keen Vision Acquisition Corporation (KVACW) Stock Performance

    • zenodo.org
    csv
    Updated Jun 27, 2024
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    Nitiraj Kulkarni; Nitiraj Kulkarni; Jagadish Tawade; Jagadish Tawade (2024). Dataset: Keen Vision Acquisition Corporation (KVACW) Stock Performance [Dataset]. http://doi.org/10.5281/zenodo.12559061
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 27, 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. f

    Antares Vision Financial Reports

    • financialreports.eu
    Updated Apr 19, 2023
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    FinancialReports UG (2023). Antares Vision Financial Reports [Dataset]. https://financialreports.eu/companies/antares-vision/
    Explore at:
    Dataset updated
    Apr 19, 2023
    Dataset authored and provided by
    FinancialReports UG
    License

    https://financialreports.eu/https://financialreports.eu/

    Time period covered
    2022 - Present
    Description

    Comprehensive collection of financial reports and documents for Antares Vision (AV)

  7. VUZI Vision: Can This Smart Glass Stock See Its Way to Success? (Forecast)

    • kappasignal.com
    Updated Jan 15, 2024
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    KappaSignal (2024). VUZI Vision: Can This Smart Glass Stock See Its Way to Success? (Forecast) [Dataset]. https://www.kappasignal.com/2024/01/vuzi-vision-can-this-smart-glass-stock.html
    Explore at:
    Dataset updated
    Jan 15, 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.

    VUZI Vision: Can This Smart Glass Stock See Its Way to Success?

    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. (INSP) Inspecs: A Clear Vision for Growth (Forecast)

    • kappasignal.com
    Updated Aug 26, 2024
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    KappaSignal (2024). (INSP) Inspecs: A Clear Vision for Growth (Forecast) [Dataset]. https://www.kappasignal.com/2024/08/insp-inspecs-clear-vision-for-growth.html
    Explore at:
    Dataset updated
    Aug 26, 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.

    (INSP) Inspecs: A Clear Vision for Growth

    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. Visa Vision: Future-Proofing Growth or Time to Reassess? (V) (Forecast)

    • kappasignal.com
    Updated Feb 3, 2024
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    KappaSignal (2024). Visa Vision: Future-Proofing Growth or Time to Reassess? (V) (Forecast) [Dataset]. https://www.kappasignal.com/2024/02/visa-vision-future-proofing-growth-or.html
    Explore at:
    Dataset updated
    Feb 3, 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.

    Visa Vision: Future-Proofing Growth or Time to Reassess? (V)

    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. Clearside Vision (CLSD): A Clear Path to Profits? (Forecast)

    • kappasignal.com
    Updated Jan 5, 2024
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    KappaSignal (2024). Clearside Vision (CLSD): A Clear Path to Profits? (Forecast) [Dataset]. https://www.kappasignal.com/2024/01/clearside-vision-clsd-clear-path-to.html
    Explore at:
    Dataset updated
    Jan 5, 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.

    Clearside Vision (CLSD): A Clear Path to Profits?

    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

  11. EYE National Vision Holdings Inc. Common Stock (Forecast)

    • kappasignal.com
    Updated Apr 7, 2023
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    KappaSignal (2023). EYE National Vision Holdings Inc. Common Stock (Forecast) [Dataset]. https://www.kappasignal.com/2023/04/eye-national-vision-holdings-inc-common.html
    Explore at:
    Dataset updated
    Apr 7, 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.

    EYE National Vision Holdings Inc. Common Stock

    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. Foresight: A Vision of Opportunity? (FTF) (Forecast)

    • kappasignal.com
    Updated May 17, 2024
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    KappaSignal (2024). Foresight: A Vision of Opportunity? (FTF) (Forecast) [Dataset]. https://www.kappasignal.com/2024/05/foresight-vision-of-opportunity-ftf.html
    Explore at:
    Dataset updated
    May 17, 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.

    Foresight: A Vision of Opportunity? (FTF)

    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. Octopus Future Vision: What's Next for OFG Stock? (Forecast)

    • kappasignal.com
    Updated Apr 3, 2024
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    KappaSignal (2024). Octopus Future Vision: What's Next for OFG Stock? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/octopus-future-vision-whats-next-for.html
    Explore at:
    Dataset updated
    Apr 3, 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.

    Octopus Future Vision: What's Next for OFG Stock?

    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. VMAR Vision Marine Technologies Inc. Common Shares (Forecast)

    • kappasignal.com
    Updated Feb 5, 2023
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    KappaSignal (2023). VMAR Vision Marine Technologies Inc. Common Shares (Forecast) [Dataset]. https://www.kappasignal.com/2023/02/vmar-vision-marine-technologies-inc.html
    Explore at:
    Dataset updated
    Feb 5, 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.

    VMAR Vision Marine Technologies Inc. Common Shares

    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. Cognex (CGNX) Stock: A Vision for Future Growth? (Forecast)

    • kappasignal.com
    Updated Apr 12, 2024
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    KappaSignal (2024). Cognex (CGNX) Stock: A Vision for Future Growth? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/cognex-cgnx-stock-vision-for-future.html
    Explore at:
    Dataset updated
    Apr 12, 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.

    Cognex (CGNX) Stock: A Vision for Future Growth?

    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. VSACU Vision Sensing Acquisition Corp. Unit (Forecast)

    • kappasignal.com
    Updated Apr 17, 2023
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    KappaSignal (2023). VSACU Vision Sensing Acquisition Corp. Unit (Forecast) [Dataset]. https://www.kappasignal.com/2023/04/vsacu-vision-sensing-acquisition-corp.html
    Explore at:
    Dataset updated
    Apr 17, 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.

    VSACU Vision Sensing Acquisition Corp. Unit

    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. EyePoint Pharmaceuticals (EYPT): A Clear Vision for Growth? (Forecast)

    • kappasignal.com
    Updated Mar 13, 2024
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    KappaSignal (2024). EyePoint Pharmaceuticals (EYPT): A Clear Vision for Growth? (Forecast) [Dataset]. https://www.kappasignal.com/2024/03/eyepoint-pharmaceuticals-eypt-clear.html
    Explore at:
    Dataset updated
    Mar 13, 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.

    EyePoint Pharmaceuticals (EYPT): A Clear Vision for Growth?

    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. Humacyte's (HUMA) Vascular Vision: Will it Reach the Finish Line? (Forecast)...

    • kappasignal.com
    Updated Dec 6, 2024
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    KappaSignal (2024). Humacyte's (HUMA) Vascular Vision: Will it Reach the Finish Line? (Forecast) [Dataset]. https://www.kappasignal.com/2024/12/humacytes-huma-vascular-vision-will-it.html
    Explore at:
    Dataset updated
    Dec 6, 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.

    Humacyte's (HUMA) Vascular Vision: Will it Reach the Finish Line?

    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. (STAA) STAAR Surgical: A Vision of Growth (Forecast)

    • kappasignal.com
    Updated Oct 15, 2024
    + more versions
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    KappaSignal (2024). (STAA) STAAR Surgical: A Vision of Growth (Forecast) [Dataset]. https://www.kappasignal.com/2024/10/staa-staar-surgical-vision-of-growth.html
    Explore at:
    Dataset updated
    Oct 15, 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.

    (STAA) STAAR Surgical: A Vision of Growth

    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. Rail Vision's (RVSN) Outlook: Analysts Predict Growth Potential. (Forecast)

    • kappasignal.com
    Updated May 3, 2025
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    KappaSignal (2025). Rail Vision's (RVSN) Outlook: Analysts Predict Growth Potential. (Forecast) [Dataset]. https://www.kappasignal.com/2025/05/rail-visions-rvsn-outlook-analysts.html
    Explore at:
    Dataset updated
    May 3, 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.

    Rail Vision's (RVSN) Outlook: Analysts Predict Growth Potential.

    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
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Nitiraj Kulkarni; Nitiraj Kulkarni; Jagadish Tawade; Jagadish Tawade (2024). Dataset: Vision Sensing Acquisition Corp. (VSACW) Stock Performance [Dataset]. http://doi.org/10.5281/zenodo.12566281
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Dataset: Vision Sensing Acquisition Corp. (VSACW) Stock Performance

Explore at:
csvAvailable download formats
Dataset updated
Jun 27, 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.

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