2 datasets found
  1. Taiwan Semiconductor: A Chip off the Old Block (TSM) (Forecast)

    • kappasignal.com
    Updated Oct 6, 2024
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    KappaSignal (2024). Taiwan Semiconductor: A Chip off the Old Block (TSM) (Forecast) [Dataset]. https://www.kappasignal.com/2024/10/taiwan-semiconductor-chip-off-old-block.html
    Explore at:
    Dataset updated
    Oct 6, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Area covered
    Taiwan
    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.

    Taiwan Semiconductor: A Chip off the Old Block (TSM)

    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. T

    Taiwan Exports

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, Taiwan Exports [Dataset]. https://tradingeconomics.com/taiwan/exports
    Explore at:
    csv, excel, json, xmlAvailable download formats
    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 31, 1981 - Oct 31, 2025
    Area covered
    Taiwan
    Description

    Exports in Taiwan increased to 61802.35 USD Million in October from 54252.93 USD Million in September of 2025. This dataset provides - Taiwan Exports - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
KappaSignal (2024). Taiwan Semiconductor: A Chip off the Old Block (TSM) (Forecast) [Dataset]. https://www.kappasignal.com/2024/10/taiwan-semiconductor-chip-off-old-block.html
Organization logo

Taiwan Semiconductor: A Chip off the Old Block (TSM) (Forecast)

Explore at:
Dataset updated
Oct 6, 2024
Dataset authored and provided by
KappaSignal
License

https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

Area covered
Taiwan
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.

Taiwan Semiconductor: A Chip off the Old Block (TSM)

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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