57 datasets found
  1. T

    Euro Area Stock Market Index (EU50) Data

    • tradingeconomics.com
    • zh.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, Euro Area Stock Market Index (EU50) Data [Dataset]. https://tradingeconomics.com/euro-area/stock-market
    Explore at:
    excel, json, 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
    Dec 31, 1986 - Jul 23, 2025
    Area covered
    Euro Area
    Description

    Euro Area's main stock market index, the EU50, rose to 5329 points on July 23, 2025, gaining 0.75% from the previous session. Over the past month, the index has climbed 0.60% and is up 9.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.

  2. United States Off Within 2 Weeks: sa: All Residential: Newark, NJ

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States Off Within 2 Weeks: sa: All Residential: Newark, NJ [Dataset]. https://www.ceicdata.com/en/united-states/off-market-within-2-weeks-by-metropolitan-areas-seasonally-adjusted/off-within-2-weeks-sa-all-residential-newark-nj
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Aug 1, 2019 - Jul 1, 2020
    Area covered
    United States
    Description

    United States Off Within 2 Weeks: sa: All Residential: Newark, NJ data was reported at 40.896 % in Jul 2020. This records an increase from the previous number of 39.285 % for Jun 2020. United States Off Within 2 Weeks: sa: All Residential: Newark, NJ data is updated monthly, averaging 23.657 % from May 2015 (Median) to Jul 2020, with 63 observations. The data reached an all-time high of 40.896 % in Jul 2020 and a record low of 16.214 % in Apr 2020. United States Off Within 2 Weeks: sa: All Residential: Newark, NJ data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

  3. u

    Socioeconomic dataset collected from open access sources for analysing...

    • fdr.uni-hamburg.de
    csv
    Updated Aug 21, 2024
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    Asthana,Shivanshi; Hölzl, Ferdinand; Shuyue, Qu; Sojung, Oh; Vergara Lopez, Leidy Gicela; Rodriguez Lopez, Juan Miguel; Asthana,Shivanshi; Hölzl, Ferdinand; Shuyue, Qu; Sojung, Oh; Vergara Lopez, Leidy Gicela (2024). Socioeconomic dataset collected from open access sources for analysing demand prediction of weekend markets in the city of Hamburg [Dataset]. http://doi.org/10.25592/uhhfdm.14807
    Explore at:
    csvAvailable download formats
    Dataset updated
    Aug 21, 2024
    Dataset provided by
    CEN, Universität Hamburg
    Authors
    Asthana,Shivanshi; Hölzl, Ferdinand; Shuyue, Qu; Sojung, Oh; Vergara Lopez, Leidy Gicela; Rodriguez Lopez, Juan Miguel; Asthana,Shivanshi; Hölzl, Ferdinand; Shuyue, Qu; Sojung, Oh; Vergara Lopez, Leidy Gicela
    License

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

    Area covered
    Hamburg
    Description

    Socioeconomic dataset for analysing demand prediction of weekend markets in the city of Hamburg, Germany

    In this DDLitlab funded Data Literacy student project, our goal was to predict weekend markets in the city of Hamburg and using open-source data and OpenStreetMaps in conjunction with Machine Learning Algorithms. You can find a brief article about the initial grant and our approach here : https://www.cliccs.uni-hamburg.de/about-cliccs/news/2023-news/2023-08-24-ddlitlab-event.html

    Github repository: https://gitlab.rrz.uni-hamburg.de/exploring-avenues-for-the-deployment-of-machine-learning-algorithms-for-sustainable-small-agricultural-business-information-using-openstreetmap/main-project-v-3

    This repository is intended to make our codes and visualisations openly available to the University of Hamburg students for further research. This is not to be used without citation under any circumstances and the University/authors deserve the right to withdraw consent at any time.

    Please do not forget to cite our work in the event of fair use.

    Organisation of our Github repository

    Codes: contains the codes for the different methods deployed for data preparation,variable selection,visualisations showing the spatial characteristics of our variables, calculating indices such as correlation coefficients and machine learning methods in increasing order of complexity. City-district (Stadtteil) as the unit of analysis.

    Data (uploaded datasets) : The open source data obtained for the project has been obtained from OpenStreetMaps (https://wiki.openstreetmap.org/wiki/Use_OpenStreetMap ) and Statistik Nord (https://www.statistik-nord.de/ ) . Each variable contains values for all stadtteils (city-districts) of Hamburg. The filenames are self explanatory.

    The Hamburg shapefile has been obtained from Geofabrik https://www.geofabrik.de/de/data/shapefiles.html In addition to the original data uploaded in the section, we have also laid down the final data we have deployed with the algorithms, in the final final_data.csv

    Our repository contains the following additional sections:

    Results: This section contains results from the codes processed in the first section. It includes the final 10 variables selected for the study, the results from the VIF analysis, correlation matrix, and some model output statistics.

    Visualisations: This section is dedicated to visualisations of the variables used for the study and the results from deployment of various methods. In case of any questions, please do not hesitate to contact us at our official student IDs : first.lastname@studium.uni-hamburg.de. We are also available on LinkedIn for professional networking in case of other queries.

    Data curators /DDLitLab data literacy project team

    Ferdinand Hölzl

    Leidy Gicela Vergara Lopez

    Shivanshi Asthana

    Shuyue Qu

    Sojung Oh

    Juan Miguel Rodriguez Lopez

  4. United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS [Dataset]. https://www.ceicdata.com/en/united-states/off-market-within-2-weeks-by-metropolitan-areas-seasonally-adjusted/off-within-2-weeks-sa-single-family-ottawa-ks
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Aug 1, 2019 - Jul 1, 2020
    Area covered
    United States
    Description

    United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS data was reported at 36.910 % in Jul 2020. This records a decrease from the previous number of 39.796 % for Jun 2020. United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS data is updated monthly, averaging 17.964 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 72.750 % in Jan 2020 and a record low of -5.969 % in May 2012. United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

  5. Forecast: Total Hours Worked in Advertising and Market Research in Canada...

    • reportlinker.com
    Updated Apr 8, 2024
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    ReportLinker (2024). Forecast: Total Hours Worked in Advertising and Market Research in Canada 2024 - 2028 [Dataset]. https://www.reportlinker.com/dataset/c0ecad0ad8162de45da3ac929a6fa2430a775ec3
    Explore at:
    Dataset updated
    Apr 8, 2024
    Dataset authored and provided by
    ReportLinker
    License

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

    Area covered
    Canada
    Description

    Forecast: Total Hours Worked in Advertising and Market Research in Canada 2024 - 2028 Discover more data with ReportLinker!

  6. C

    Weekly market delivery service Ulm

    • ckan.mobidatalab.eu
    • processor1.francecentral.cloudapp.azure.com
    Updated Jun 22, 2023
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    Digitale Agenda - Stadt Ulm (2023). Weekly market delivery service Ulm [Dataset]. https://ckan.mobidatalab.eu/dataset/gpsdataweeklymarketdeliveryservice
    Explore at:
    http://publications.europa.eu/resource/authority/file-type/geojson(387618)Available download formats
    Dataset updated
    Jun 22, 2023
    Dataset provided by
    Digitale Agenda - Stadt Ulm
    License

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

    Description

    The data record contains the tracker data of the delivery vehicle from mein-Wochenmarkt.online from 02/21. The data set contains the GPS data from several market days (02/19/20, 02/21/20, 03/21/20) and was edited in such a way that the exact start and end points are not visible in order to protect the address data of customers. mein-Wochenmarkt.online is a startup that existed between February and December 2020 and delivered goods from the weekly market in Ulm and from the weekly market on Eselsberg to your door on market days. ### Data source: Datahub City of Ulm under CCZero

  7. Statistical results of weekly frequency.

    • plos.figshare.com
    xls
    Updated May 30, 2023
    + more versions
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    Haijun Yang; Shuheng Chen (2023). Statistical results of weekly frequency. [Dataset]. http://doi.org/10.1371/journal.pone.0197935.t005
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Haijun Yang; Shuheng Chen
    License

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

    Description

    Statistical results of weekly frequency.

  8. United States Off Within 2 Weeks: All Residential: New York, NY

    • ceicdata.com
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    CEICdata.com, United States Off Within 2 Weeks: All Residential: New York, NY [Dataset]. https://www.ceicdata.com/en/united-states/off-market-within-2-weeks-by-metropolitan-areas/off-within-2-weeks-all-residential-new-york-ny
    Explore at:
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Aug 1, 2019 - Jul 1, 2020
    Area covered
    United States
    Description

    United States Off Within 2 Weeks: All Residential: New York, NY data was reported at 24.912 % in Jul 2020. This records a decrease from the previous number of 30.188 % for Jun 2020. United States Off Within 2 Weeks: All Residential: New York, NY data is updated monthly, averaging 17.076 % from May 2015 (Median) to Jul 2020, with 63 observations. The data reached an all-time high of 30.834 % in Jun 2017 and a record low of 8.213 % in Apr 2020. United States Off Within 2 Weeks: All Residential: New York, NY data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB010: Off Market Within 2 Weeks: by Metropolitan Areas.

  9. Where Will CLPT Stock Be in 16 Weeks? (Forecast)

    • kappasignal.com
    Updated Aug 31, 2023
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    KappaSignal (2023). Where Will CLPT Stock Be in 16 Weeks? (Forecast) [Dataset]. https://www.kappasignal.com/2023/08/where-will-clpt-stock-be-in-16-weeks.html
    Explore at:
    Dataset updated
    Aug 31, 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.

    Where Will CLPT Stock Be in 16 Weeks?

    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. w

    Dataset of books series that contain Bear market investing strategies

    • workwithdata.com
    Updated Nov 25, 2024
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    Work With Data (2024). Dataset of books series that contain Bear market investing strategies [Dataset]. https://www.workwithdata.com/datasets/book-series?f=1&fcol0=j0-book&fop0=%3D&fval0=Bear+market+investing+strategies&j=1&j0=books
    Explore at:
    Dataset updated
    Nov 25, 2024
    Dataset authored and provided by
    Work With Data
    License

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

    Description

    This dataset is about book series. It has 1 row and is filtered where the books is Bear market investing strategies. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

  11. Weekly Market and Economics Roundup for the week ended 1 September 2017

    • researchdata.edu.au
    • data.nsw.gov.au
    Updated Sep 8, 2021
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    data.nsw.gov.au (2021). Weekly Market and Economics Roundup for the week ended 1 September 2017 [Dataset]. https://researchdata.edu.au/weekly-market-economics-september-2017/1758561
    Explore at:
    Dataset updated
    Sep 8, 2021
    Dataset provided by
    Government of New South Waleshttp://nsw.gov.au/
    Description

    This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

  12. d

    LRM05 - Average Net Weekly Live Register Changes

    • datasalsa.com
    csv, json-stat, px +1
    Updated Jan 4, 2022
    + more versions
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    Central Statistics Office (2022). LRM05 - Average Net Weekly Live Register Changes [Dataset]. https://datasalsa.com/dataset/?catalogue=data.gov.ie&name=lrm05-average-net-weekly-live-register-changes
    Explore at:
    csv, json-stat, px, xlsxAvailable download formats
    Dataset updated
    Jan 4, 2022
    Dataset authored and provided by
    Central Statistics Office
    License

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

    Time period covered
    Jan 4, 2022
    Description

    LRM05 - Average Net Weekly Live Register Changes. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Average Net Weekly Live Register Changes...

  13. Weekly Market and Economics Roundup for the week ended 16 October 2015

    • data.nsw.gov.au
    pdf
    Updated Sep 8, 2021
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    NSW Government (2021). Weekly Market and Economics Roundup for the week ended 16 October 2015 [Dataset]. https://data.nsw.gov.au/data/dataset/3-14939-weekly-market-and-economics-roundup-for-the-week-ended-16-october-2015
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Sep 8, 2021
    Dataset provided by
    Government of New South Waleshttp://nsw.gov.au/
    Description

    This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

  14. United States Off Within 2 Weeks: Multi-Family: Detroit, MI

    • ceicdata.com
    Updated Mar 15, 2025
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    CEICdata.com (2025). United States Off Within 2 Weeks: Multi-Family: Detroit, MI [Dataset]. https://www.ceicdata.com/en/united-states/off-market-within-2-weeks-by-metropolitan-areas/off-within-2-weeks-multifamily-detroit-mi
    Explore at:
    Dataset updated
    Mar 15, 2025
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Aug 1, 2019 - Jul 1, 2020
    Area covered
    United States
    Description

    United States Off Within 2 Weeks: Multi-Family: Detroit, MI data was reported at 50.847 % in Jul 2020. This records an increase from the previous number of 47.500 % for Jun 2020. United States Off Within 2 Weeks: Multi-Family: Detroit, MI data is updated monthly, averaging 36.788 % from Feb 2013 (Median) to Jul 2020, with 90 observations. The data reached an all-time high of 56.364 % in Apr 2019 and a record low of 6.452 % in Dec 2014. United States Off Within 2 Weeks: Multi-Family: Detroit, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB010: Off Market Within 2 Weeks: by Metropolitan Areas.

  15. MNP Stock Forecast: A Buy For The Next 8 Weeks (Forecast)

    • kappasignal.com
    Updated Jun 19, 2023
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    KappaSignal (2023). MNP Stock Forecast: A Buy For The Next 8 Weeks (Forecast) [Dataset]. https://www.kappasignal.com/2023/06/mnp-stock-forecast-buy-for-next-8-weeks.html
    Explore at:
    Dataset updated
    Jun 19, 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.

    MNP Stock Forecast: A Buy For The Next 8 Weeks

    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. LON:ENSI Stock Forecast: A Buy For The Next 16 Weeks (Forecast)

    • kappasignal.com
    Updated Oct 16, 2023
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    KappaSignal (2023). LON:ENSI Stock Forecast: A Buy For The Next 16 Weeks (Forecast) [Dataset]. https://www.kappasignal.com/2023/10/lonensi-stock-forecast-buy-for-next-16.html
    Explore at:
    Dataset updated
    Oct 16, 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:ENSI Stock Forecast: A Buy For The Next 16 Weeks

    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. CBAN Stock Forecast: A Hold For The Next 4 Weeks (Forecast)

    • kappasignal.com
    Updated Dec 1, 2023
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    KappaSignal (2023). CBAN Stock Forecast: A Hold For The Next 4 Weeks (Forecast) [Dataset]. https://www.kappasignal.com/2023/12/cban-stock-forecast-hold-for-next-4.html
    Explore at:
    Dataset updated
    Dec 1, 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.

    CBAN Stock Forecast: A Hold For The Next 4 Weeks

    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. Where Will BSFC Stock Be in 16 Weeks? (Forecast)

    • kappasignal.com
    Updated Oct 16, 2023
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    KappaSignal (2023). Where Will BSFC Stock Be in 16 Weeks? (Forecast) [Dataset]. https://www.kappasignal.com/2023/10/where-will-bsfc-stock-be-in-16-weeks.html
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    Dataset updated
    Oct 16, 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.

    Where Will BSFC Stock Be in 16 Weeks?

    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. United States SB: NJ: Outlook: FN: Identify Potential Markets for Exporting

    • ceicdata.com
    Updated Apr 11, 2022
    + more versions
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    CEICdata.com (2022). United States SB: NJ: Outlook: FN: Identify Potential Markets for Exporting [Dataset]. https://www.ceicdata.com/en/united-states/small-business-pulse-survey-by-state-northeast-region/sb-nj-outlook-fn-identify-potential-markets-for-exporting
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    Dataset updated
    Apr 11, 2022
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Dec 27, 2021 - Apr 11, 2022
    Area covered
    United States
    Description

    United States SB: NJ: Outlook: FN: Identify Potential Markets for Exporting data was reported at 5.300 % in 11 Apr 2022. This records an increase from the previous number of 4.000 % for 04 Apr 2022. United States SB: NJ: Outlook: FN: Identify Potential Markets for Exporting data is updated weekly, averaging 3.550 % from Nov 2021 (Median) to 11 Apr 2022, with 18 observations. The data reached an all-time high of 5.300 % in 11 Apr 2022 and a record low of 2.400 % in 14 Mar 2022. United States SB: NJ: Outlook: FN: Identify Potential Markets for Exporting data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S049: Small Business Pulse Survey: by State: Northeast Region: Weekly, Beg Monday (Discontinued).

  20. United States Off Within 2 Weeks: Townhouse: Brainerd, MN

    • ceicdata.com
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    CEICdata.com, United States Off Within 2 Weeks: Townhouse: Brainerd, MN [Dataset]. https://www.ceicdata.com/en/united-states/off-market-within-2-weeks-by-metropolitan-areas/off-within-2-weeks-townhouse-brainerd-mn
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    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Jul 1, 2019 - Jul 1, 2020
    Area covered
    United States
    Description

    United States Off Within 2 Weeks: Townhouse: Brainerd, MN data was reported at 71.429 % in Jul 2020. This records an increase from the previous number of 40.000 % for Jun 2020. United States Off Within 2 Weeks: Townhouse: Brainerd, MN data is updated monthly, averaging 33.333 % from Mar 2012 (Median) to Jul 2020, with 90 observations. The data reached an all-time high of 100.000 % in Feb 2020 and a record low of 0.000 % in Dec 2019. United States Off Within 2 Weeks: Townhouse: Brainerd, MN data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB010: Off Market Within 2 Weeks: by Metropolitan Areas.

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TRADING ECONOMICS, Euro Area Stock Market Index (EU50) Data [Dataset]. https://tradingeconomics.com/euro-area/stock-market

Euro Area Stock Market Index (EU50) Data

Euro Area Stock Market Index (EU50) - Historical Dataset (1986-12-31/2025-07-23)

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6 scholarly articles cite this dataset (View in Google Scholar)
excel, json, 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
Dec 31, 1986 - Jul 23, 2025
Area covered
Euro Area
Description

Euro Area's main stock market index, the EU50, rose to 5329 points on July 23, 2025, gaining 0.75% from the previous session. Over the past month, the index has climbed 0.60% and is up 9.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.

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