The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Discover how data center investments, driven by AI advancements, are projected to boost U.S. economic growth, with major tech companies leading the charge.

The Gross Domestic Product (GDP) in the United States contracted 0.50 percent in the first quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for St. Louis Fed Economic News Index: Real GDP Nowcast (STLENI) from Q2 2013 to Q2 2025 about nowcast, projection, real, GDP, rate, indexes, and USA.

Live Briefs Investor – US Covering thousands of listed securities and events across 80 news categories, Live Briefs Investor US is specifically designed to keep individual investors and active traders on top of breaking news that is likely to affect their portfolios.

Most of the largest and most respected retail and self-directed brokerage firms in the North America rely on MT Newswires to provide their clients with complete coverage of the financial markets. The Investor service includes timely and insightful commentary on equities, commodities, ETFs, economics, forex, options and fixed income assets throughout the day (6:30 am to 6:30 pm EST).

Every story is ticker-tagged and category-coded to allow for seamless platform integration. US Equities – significant events affecting individual public companies in the US: After-hours and pre-market news, trading activity and technical price level indications; Earnings estimate change alerts; Analyst Rating Changes- the most comprehensive view and coverage of rating changes available anywhere; ETF Power Play – daily trends in ETF trading activity; Mini and detailed sector summaries – pre-market, mid-day, and closing; Market Chatter – real-time coverage of trading desk rumors and breaking news; Zero noise: Only premium, original news and event analysis. Never any fillers (press releases, non-market related news, etc.).

By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

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.

This dataset consists of approximately 8,000 news articles focused on the US Economy. Each article has been tagged for its relevance to the US Economy and analysed for its tone. This dataset is particularly useful for Natural Language Processing (NLP) tasks, offering a valuable resource for sentiment analysis, text classification, and information extraction related to economic news.

Columns

• _unit_id: A unique identifier for each unit or article.
• _golden: Indicates whether the entry is a golden record.
• _unit_state: The state of the unit.
• _trusted_judgments: A factor indicating the trustworthiness of the judgments made on the data.
• _last_judgment_at: The timestamp of the last judgment on the article.
• positivity: A score indicating the positivity of the article's tone.
• positivity:confidence: The confidence level associated with the positivity score.
• relevance: Indicates whether the article is relevant to the US Economy (e.g., 'yes' or 'no').
• relevance:confidence: The confidence level associated with the relevance tag.
• articleid: A unique identifier for the news article.

Distribution

The dataset typically comes in a CSV format. It contains approximately 8,000 news articles, with the data spanning from 17th November 2015 to 6th December 2015. Specific numbers for rows or records are available, with 8,000 entries represented in total.

Usage

This dataset is ideal for various applications, including: * Natural Language Processing (NLP) research and model training. * Developing sentiment analysis models for economic news. * Creating tools for economic news classification (relevant/not relevant). * Analysing trends in economic media coverage and public sentiment. * Training machine learning models for text mining in finance and economics.

Coverage

The dataset's focus is on US economic news articles. The articles cover a time range from 17th November 2015 to 6th December 2015. While the content is specific to the US Economy, the dataset itself is listed with a global region for distribution, meaning it can be accessed worldwide. There are no specific notes on demographic scope within the articles themselves.

License

CCO

Who Can Use It

• Data Scientists and AI/ML Researchers: For developing and testing NLP models on real-world economic text.
• Economists and Financial Analysts: To gain insights into how economic events are portrayed in the media and their perceived relevance.
• Academics and Students: For research projects on economic news sentiment, text analysis, and media studies.
• Media Monitoring Professionals: To track and analyse the coverage of economic topics.

Dataset Name Suggestions

• US Economic News Relevance and Tone Dataset
• US Economic News Articles for NLP
• Economic News Sentiment Analysis Dataset (US)
• US Economy News Articles (2015)

Attributes

Original Data Source:US Economic News Articles (Useful for NLP)

This dataset features financial news headlines collected from leading financial news websites, including CNBC, The Guardian, and Reuters. It provides an overview of the U.S. economy and stock market, primarily reflecting daily market sentiment over several years. The main purpose of this dataset is to facilitate Natural Language Processing (NLP) analyses to explore the correlation between the positivity or negativity of news sentiment and U.S. stock market performance, such as gains and losses. It is ideal for data scientists and analysts keen on understanding market dynamics through textual data.

Columns

The dataset typically includes the following columns, though availability may vary slightly by source: * Headlines: The main title or headline of the financial article. * Time: The last updated date and time of the article. * Description: A preview or summary text of the article's content.

Distribution

The data files are generally provided in CSV format. Specific numbers for rows or records are not available within the provided sources, but the dataset is structured to allow for easy processing and analysis.

Usage

This dataset is well-suited for a variety of applications, including: * Sentiment analysis of financial news to predict market movements. * Developing and testing Natural Language Processing (NLP) models. * Data science and analytics projects focused on economic trends and stock market performance. * Research into the impact of media on financial markets.

Coverage

The dataset covers news related to the U.S. economy and stock market. * Time Range: * CNBC and The Guardian data spans from late December 2017 to 19th July 2020. * Reuters data covers from late March 2018 to 19th July 2020. * Collectively, the headlines reflect an overview of the U.S. economy and stock market for approximately one to two years from their scraping date.

License

CCO

Who Can Use It

This dataset is intended for a range of users, including: * Data Scientists and Analysts performing market sentiment analysis. * Researchers studying economic indicators and financial news impact. * Individuals interested in Natural Language Processing (NLP) and text analysis applications in finance. * Anyone looking to gain insights into the relationship between news sentiment and stock market performance.

Dataset Name Suggestions

• US Financial News Headlines
• Stock Market Sentiment News
• Financial Article Headlines
• Daily Market News Dataset
• Economy News Headlines for NLP

Attributes

Original Data Source: Financial News Headlines Data

Gallup's Economic Confidence Index is based on the combined responses to two questions, the first asking Americans to rate economic conditions in this country today, and second, whether they think economic conditions in the country as a whole are getting better or getting worse. Results are based on telephone interviews with approximately 3,500 national adults; margin of error is ±2 percentage points.

The Gross Domestic Product (GDP) in the United States expanded 2 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - United States GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The U.S. Economy on a Growth Trajectory, but Risks Loom

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.

The US Economy: A House of Cards?

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

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Jun 2025 about recession indicators, academic data, and USA.

Government spending in the United States was last recorded at 39.7 percent of GDP in 2024 . This dataset provides - United States Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Content

St. Louis Fed’s Economic News Index (ENI) uses economic content from key monthly economic data releases to forecast the growth of real GDP during that quarter. In general, the most-current observation is revised multiple times throughout the quarter. The final forecasted value (before the BEA’s release of the advance estimate of GDP) is the static, historical value for that quarter. For more information, see Grover, Sean P.; Kliesen, Kevin L.; and McCracken, Michael W. “A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth" (https://research.stlouisfed.org/publications/review/2016/12/05/a-macroeconomic-news-index-for-constructing-nowcasts-of-u-s-real-gross-domestic-product-growth/ )

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2013-04-01

• Observation End : 2019-10-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Ferdinand Stöhr on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Context Scraped from CNBC, the Guardian, and Reuters official websites, the headlines in these datasets reflects the overview of the U.S. economy and stock market every day for the past year to 2 years.

Content Data scraped from CNBC contains the headlines, last updated date, and the preview text of articles from the end of December 2017 to July 19th, 2020. Data scraped from the Guardian Business contains the headlines and last updated date of articles from the end of December 2017 to July 19th, 2020 since the Guardian Business does not offer preview text. Data scraped from Reuters contains the headlines, last updated date, and the preview text of articles from the end of March 2018 to July 19th, 2020. Inspiration I firmly believe that the sentiment of financial news articles reflects and directs the performance of the U.S. stock market. Therefore, by applying Natural Language Processing (NLP) through these headlines, I can see how the positivity/negativity of the score through each day correlate to the stock market's gains/losses.

The cover image was taken from https://hipwallpaper.com/stock-trader-wallpapers/

Original Data Source: Financial News Headlines Data

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

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.

US Economy to Slow in June as Fed Tightens Monetary Policy

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