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.

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.

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

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.

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

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

Statistical results of weekly frequency.

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.

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

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.

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

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...

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

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.

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

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

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

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

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).

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.