2 datasets found
  1. k

    What is the relationship between unemployment and inflation? (Forecast)

    • kappasignal.com
    Updated Dec 21, 2023
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    KappaSignal (2023). What is the relationship between unemployment and inflation? (Forecast) [Dataset]. https://www.kappasignal.com/2023/12/what-is-relationship-between.html
    Explore at:
    Dataset updated
    Dec 21, 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.

    What is the relationship between unemployment and inflation?

    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. B

    Canadian Gallup Poll, September 1981, #453_1

    • borealisdata.ca
    Updated Jun 23, 2023
    + more versions
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    Gallup Canada (2023). Canadian Gallup Poll, September 1981, #453_1 [Dataset]. http://doi.org/10.5683/SP2/D1KZDB
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 23, 2023
    Dataset provided by
    Borealis
    Authors
    Gallup Canada
    License

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

    Area covered
    Canada
    Description

    This Gallup poll seeks the opinions of Canadians, on predominantly political issues. The questions ask opinions about political leaders and political issues within the country. There are also questions on other topics of interest and importance to the country and government, such as allowing workers to strike, saying grace and interest rates. The respondents were also asked questions so that they could be grouped according to geographic and social variables. Topics of interest include: allowing essential workers to strike; approval of Ed Broadbent as NDP leader; approval of Joe Clark as leader of the Conservative party; approval of Trudeau as Prime Minister; the benefits of following the public view; cutting passenger rail services; effects of the high interest rate; giving workers the right to strike; naming the Lieutenant Governor; opinions about abolishing the Governor General position; opinions about abolishing the Lieutenant Governor position; preference of the name Canada day over Dominion day; protest votes; and saying grace out loud before family meals. Basic demographic variables are also included.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
KappaSignal (2023). What is the relationship between unemployment and inflation? (Forecast) [Dataset]. https://www.kappasignal.com/2023/12/what-is-relationship-between.html

What is the relationship between unemployment and inflation? (Forecast)

Explore at:
Dataset updated
Dec 21, 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.

What is the relationship between unemployment and inflation?

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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