60 datasets found
  1. T

    United States Unemployment Rate

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 3, 2025
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    TRADING ECONOMICS (2025). United States Unemployment Rate [Dataset]. https://tradingeconomics.com/united-states/unemployment-rate
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    excel, xml, csv, jsonAvailable download formats
    Dataset updated
    Jul 3, 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 31, 1948 - Jun 30, 2025
    Area covered
    United States
    Description

    Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  2. T

    Germany Unemployment Rate

    • tradingeconomics.com
    • es.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 1, 2025
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    TRADING ECONOMICS (2025). Germany Unemployment Rate [Dataset]. https://tradingeconomics.com/germany/unemployment-rate
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    csv, xml, json, excelAvailable download formats
    Dataset updated
    Jul 1, 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 31, 1950 - Jun 30, 2025
    Area covered
    Germany
    Description

    Unemployment Rate in Germany remained unchanged at 6.30 percent in June. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  3. T

    Denmark Net Unemployment Rate

    • tradingeconomics.com
    • tr.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 31, 2025
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    TRADING ECONOMICS (2025). Denmark Net Unemployment Rate [Dataset]. https://tradingeconomics.com/denmark/unemployment-rate
    Explore at:
    csv, xml, json, excelAvailable download formats
    Dataset updated
    May 31, 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 31, 1980 - May 31, 2025
    Area covered
    Denmark
    Description

    Unemployment Rate in Denmark remained unchanged at 2.50 percent in May. This dataset provides - Denmark Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  4. Unemployment during the economic downturn

    • data.wu.ac.at
    • gimi9.com
    • +1more
    html
    Updated Jan 26, 2016
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    Office for National Statistics (2016). Unemployment during the economic downturn [Dataset]. https://data.wu.ac.at/schema/data_gov_uk/NTY0OGRiNTEtMTkwYS00MjE3LWE5MTItZmY4ZDE5ZjYzYjc4
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Jan 26, 2016
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    This release looks at the increase in unemployment during the recent economic downturn. Increases in unemployment will be compared across regions in the UK, age groups, gender and other characteristics. Claimant count data will also be included.

    Source agency: Office for National Statistics

    Designation: National Statistics

    Language: English

    Alternative title: Unemployment during the economic downturn

  5. J

    Mismatch Shocks and Unemployment During the Great Recession (replication...

    • journaldata.zbw.eu
    Updated Nov 22, 2022
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    Francesco Furlanetto; Nicolas Groshenny; Francesco Furlanetto; Nicolas Groshenny (2022). Mismatch Shocks and Unemployment During the Great Recession (replication data) [Dataset]. https://journaldata.zbw.eu/dataset/mismatch-shocks-and-unemployment-during-the-great-recession?activity_id=14d13b43-245e-4d48-a4ff-643ff741acb4
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    Dataset updated
    Nov 22, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Francesco Furlanetto; Nicolas Groshenny; Francesco Furlanetto; Nicolas Groshenny
    License

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

    Description

    We investigate the macroeconomic consequences of fluctuations in the effectiveness of the labor market matching process with a focus on the Great Recession. We conduct our analysis in the context of an estimated medium-scale dynamic stochastic general equilibrium model with sticky prices and equilibrium search unemployment that features a shock to the matching efficiency (or mismatch shock). We find that this shock is not important for unemployment fluctuations in normal times. However, it plays a somewhat larger role during the Great Recession when it contributes to raise the actual unemployment rate by around 1.3 percentage points and the natural rate by around 2 percentage points. The mismatch shock is the dominant driver of the natural rate of unemployment and explains part of the recent shift of the Beveridge curve.

  6. T

    Japan Unemployment Rate

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 26, 2025
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    TRADING ECONOMICS (2025). Japan Unemployment Rate [Dataset]. https://tradingeconomics.com/japan/unemployment-rate
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    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    Jun 26, 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 31, 1953 - May 31, 2025
    Area covered
    Japan
    Description

    Unemployment Rate in Japan remained unchanged at 2.50 percent in May. This dataset provides the latest reported value for - Japan Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  7. F

    Unemployment Rate in New York

    • fred.stlouisfed.org
    json
    Updated Jun 25, 2025
    + more versions
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    (2025). Unemployment Rate in New York [Dataset]. https://fred.stlouisfed.org/series/NYUR
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    jsonAvailable download formats
    Dataset updated
    Jun 25, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    New York
    Description

    Graph and download economic data for Unemployment Rate in New York (NYUR) from Jan 1976 to May 2025 about NY, unemployment, rate, and USA.

  8. m

    Data for: The cyclicality of the separation and job finding rates in France

    • data.mendeley.com
    • narcis.nl
    Updated Nov 30, 2016
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    Thomas Le Barbanchon (2016). Data for: The cyclicality of the separation and job finding rates in France [Dataset]. http://doi.org/10.17632/m73vfyyvnp.1
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    Dataset updated
    Nov 30, 2016
    Authors
    Thomas Le Barbanchon
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Area covered
    France
    Description

    Abstract of associated article: In this paper, we shed light on the relative contribution of the separation and job finding rates to French unemployment at business cycle frequencies by using administrative data on registered unemployment and labor force surveys. We first investigate the fluctuations in steady state unemployment, and then in current unemployment in order to take into account the unemployment deviations from equilibrium. Both data sets lead to quite similar results. Both rates contributed to unemployment fluctuations during the nineties (50:50 split), while in the last decade the job finding rate was more significant and explained around 65% of the French unemployment fluctuations. In particular, the last business cycle episode, including the last recession, exacerbated the role of the job finding rate. We then show that the predominant role of the job finding rate in the last decade holds when the economy is hit by aggregate business cycle shocks, moving unemployment and vacancies along the Beveridge curve.

  9. F

    Initial Claims

    • fred.stlouisfed.org
    json
    Updated Jul 17, 2025
    + more versions
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    (2025). Initial Claims [Dataset]. https://fred.stlouisfed.org/series/ICSA
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    jsonAvailable download formats
    Dataset updated
    Jul 17, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Initial Claims (ICSA) from 1967-01-07 to 2025-07-12 about initial claims, headline figure, and USA.

  10. T

    United States Long Term Unemployment Rate

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Long Term Unemployment Rate [Dataset]. https://tradingeconomics.com/united-states/long-term-unemployment-rate
    Explore at:
    json, excel, csv, 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, 1948 - Jun 30, 2025
    Area covered
    United States
    Description

    Long Term Unemployment Rate in the United States increased to 0.97 percent in June from 0.85 percent in May of 2025. This dataset provides the latest reported value for - United States Long Term Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  11. Understanding the Dynamics and Implications of a Housing Market Recession...

    • kappasignal.com
    Updated May 25, 2023
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    KappaSignal (2023). Understanding the Dynamics and Implications of a Housing Market Recession (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/understanding-dynamics-and-implications.html
    Explore at:
    Dataset updated
    May 25, 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.

    Understanding the Dynamics and Implications of a Housing Market Recession

    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. Unemployment rate in India 2024

    • statista.com
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    Statista, Unemployment rate in India 2024 [Dataset]. https://www.statista.com/statistics/271330/unemployment-rate-in-india/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    1999 - 2024
    Area covered
    India
    Description

    The statistic shows the unemployment rate in India from 1999 to 2024. In 2024, the unemployment rate in India was estimated to be 4.2 percent. India's economy in comparison to other BRIC states India possesses one of the fastest-growing economies in the world and as a result, India is recognized as one of the G-20 major economies as well as a member of the BRIC countries, an association that is made up of rapidly growing economies. As well as India, three other countries, namely Brazil, Russia and China, are BRIC members. India’s manufacturing industry plays a large part in the development of its economy; however its services industry is the most significant economical factor. The majority of the population of India works in this sector. India’s notable economic boost can be attributed to significant gains over the past decade in regards to the efficiency of the production of goods as well as maintaining relatively low debt, particularly when compared to the total amount earned from goods and services produced throughout the years. When considering individual development as a country, India progressed significantly over the years. However, in comparison to the other emerging countries in the BRIC group, India’s progress was rather minimal. While China experienced the most apparent growth, India’s efficiency and productivity remained somewhat stagnant over the course of 3 or 4 years. India also reported a rather large trade deficit over the past decade, implying that its total imports exceeded its total amount of exports, essentially forcing the country to borrow money in order to finance the nation. Most economists consider trade deficits a negative factor, especially in the long run and for developing or emerging countries.

  13. T

    Canada Unemployment Rate

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 11, 2025
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    TRADING ECONOMICS (2025). Canada Unemployment Rate [Dataset]. https://tradingeconomics.com/canada/unemployment-rate
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    Jul 11, 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 31, 1966 - Jun 30, 2025
    Area covered
    Canada
    Description

    Unemployment Rate in Canada decreased to 6.90 percent in June from 7 percent in May of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  14. LON:ETX Stock: Are We Headed for a Recession? (Forecast)

    • kappasignal.com
    Updated Nov 4, 2023
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    KappaSignal (2023). LON:ETX Stock: Are We Headed for a Recession? (Forecast) [Dataset]. https://www.kappasignal.com/2023/11/lonetx-stock-are-we-headed-for-recession.html
    Explore at:
    Dataset updated
    Nov 4, 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.

    LON:ETX Stock: Are We Headed for a Recession?

    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. DTRTU Stock: Are We Headed for a Recession? (Forecast)

    • kappasignal.com
    Updated Nov 4, 2023
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    KappaSignal (2023). DTRTU Stock: Are We Headed for a Recession? (Forecast) [Dataset]. https://www.kappasignal.com/2023/11/dtrtu-stock-are-we-headed-for-recession.html
    Explore at:
    Dataset updated
    Nov 4, 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.

    DTRTU Stock: Are We Headed for a Recession?

    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. f

    Pregnancy-Induced Hypertensive Disorders before and after a National...

    • figshare.com
    • plos.figshare.com
    docx
    Updated Jan 15, 2016
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    Védís Helga Eiríksdóttir; Unnur Anna Valdimarsdóttir; Tinna Laufey Ásgeirsdóttir; Arna Hauksdóttir; Sigrún Helga Lund; Ragnheiður Ingibjörg Bjarnadóttir; Sven Cnattingius; Helga Zoëga (2016). Pregnancy-Induced Hypertensive Disorders before and after a National Economic Collapse: A Population Based Cohort Study [Dataset]. http://doi.org/10.1371/journal.pone.0138534
    Explore at:
    docxAvailable download formats
    Dataset updated
    Jan 15, 2016
    Dataset provided by
    PLOS ONE
    Authors
    Védís Helga Eiríksdóttir; Unnur Anna Valdimarsdóttir; Tinna Laufey Ásgeirsdóttir; Arna Hauksdóttir; Sigrún Helga Lund; Ragnheiður Ingibjörg Bjarnadóttir; Sven Cnattingius; Helga Zoëga
    License

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

    Description

    BackgroundData on the potential influence of macroeconomic recessions on maternal diseases during pregnancy are scarce. We aimed to assess potential change in prevalence of pregnancy-induced hypertensive disorders (preeclampsia and gestational hypertension) during the first years of the major national economic recession in Iceland, which started abruptly in October 2008.Methods and FindingsWomen whose pregnancies resulted in live singleton births in Iceland in 2005–2012 constituted the study population (N = 35,211). Data on pregnancy-induced hypertensive disorders were obtained from the Icelandic Medical Birth Register and use of antihypertensive drugs during pregnancy, including β-blockers and calcium channel blockers, from the Icelandic Medicines Register. With the pre-collapse period as reference, we used logistic regression analysis to assess change in pregnancy-induced hypertensive disorders and use of antihypertensives during the first four years after the economic collapse, adjusting for demographic and pregnancy characteristics, taking aggregate economic indicators into account. Compared with the pre-collapse period, we observed an increased prevalence of gestational hypertension in the first year following the economic collapse (2.4% vs. 3.9%; adjusted odds ratio [aOR] 1.47; 95 percent confidence interval [95%CI] 1.13–1.91) but not in the subsequent years. The association disappeared completely when we adjusted for aggregate unemployment rate (aOR 1.04; 95% CI 0.74–1.47). Similarly, there was an increase in prescription fills of β-blockers in the first year following the collapse (1.9% vs.3.1%; aOR 1.43; 95% CI 1.07–1.90), which disappeared after adjusting for aggregate unemployment rate (aOR 1.05; 95% CI 0.72–1.54). No changes were observed for preeclampsia or use of calcium channel blockers between the pre- and post-collapse periods.ConclusionsOur data suggest a transient increased risk of gestational hypertension and use of β-blockers among pregnant women in Iceland in the first and most severe year of the national economic recession.

  17. TA:TSX Stock: Are We Headed for a Recession? (Forecast)

    • kappasignal.com
    Updated Aug 22, 2023
    + more versions
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    KappaSignal (2023). TA:TSX Stock: Are We Headed for a Recession? (Forecast) [Dataset]. https://www.kappasignal.com/2023/08/tatsx-stock-are-we-headed-for-recession.html
    Explore at:
    Dataset updated
    Aug 22, 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.

    TA:TSX Stock: Are We Headed for a Recession?

    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. LON:BA29 Stock: Are We Headed for a Recession? (Forecast)

    • kappasignal.com
    Updated Jul 9, 2023
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    KappaSignal (2023). LON:BA29 Stock: Are We Headed for a Recession? (Forecast) [Dataset]. https://www.kappasignal.com/2023/07/lonba29-stock-are-we-headed-for.html
    Explore at:
    Dataset updated
    Jul 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.

    LON:BA29 Stock: Are We Headed for a Recession?

    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. if the stock market goes down during a recession, you should sell all of...

    • kappasignal.com
    Updated May 6, 2023
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    KappaSignal (2023). if the stock market goes down during a recession, you should sell all of your investments to minimize your losses. (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/if-stock-market-goes-down-during.html
    Explore at:
    Dataset updated
    May 6, 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.

    if the stock market goes down during a recession, you should sell all of your investments to minimize your losses.

    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. MMI Stock: Are We Headed for a Recession? (Forecast)

    • kappasignal.com
    Updated Dec 11, 2023
    + more versions
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    KappaSignal (2023). MMI Stock: Are We Headed for a Recession? (Forecast) [Dataset]. https://www.kappasignal.com/2023/12/mmi-stock-are-we-headed-for-recession.html
    Explore at:
    Dataset updated
    Dec 11, 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.

    MMI Stock: Are We Headed for a Recession?

    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
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
TRADING ECONOMICS (2025). United States Unemployment Rate [Dataset]. https://tradingeconomics.com/united-states/unemployment-rate

United States Unemployment Rate

United States Unemployment Rate - Historical Dataset (1948-01-31/2025-06-30)

Explore at:
128 scholarly articles cite this dataset (View in Google Scholar)
excel, xml, csv, jsonAvailable download formats
Dataset updated
Jul 3, 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 31, 1948 - Jun 30, 2025
Area covered
United States
Description

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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