The Gross Domestic Product (GDP) in the United States was worth 27720.71 billion US dollars in 2023, according to official data from the World Bank. The GDP value of the United States represents 26.29 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.

US Economic news articles with tone and relevance The dataset consists of approximately 8000 news articles, which were tagged as relevant or not relevant to the US Economy.

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

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.

The main stock market index of United States, the US500, rose to 6173 points on June 27, 2025, gaining 0.52% from the previous session. Over the past month, the index has climbed 4.83% and is up 13.05% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

Unemployment Rate in the United States remained unchanged at 4.20 percent in May. 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 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

China boasts the fastest growing GDP of all developed nations. Neighboring regions will have the largest middle class in history. China is building transport infrastructure to take advantage. Companies that capture market share in this region will be the largest and best performing over the next decade.

Macro Tailwinds

1) China GDP is the fastest growing of any major country with expected 5-6% over the next decade. If businesses (Alibaba, Tencent, etc..) maintain flat market share, that alone will drive 5-6% over the next decade. This is already higher than JP Morgans expectation (from their 13f filings) that the US market will perform between -5% and +5% over this coming decade.

2) The Southeast Asia Region contains about 5 billion people. China is constructing the One Best One Road which will be completed by 2030. This will grant their businesses access to the fastest and largest growing middle class in human history. Over the next 10+ years this region will be home to the largest middle class in history, potentially over 10x that of North America and Europe, based on stock price in Google Sheets.

Increasing average Chinese income.

Chinese average income has more than doubled over the last decade. Having sustained the least economic damage from the virus, this trend is expected to continue. At this pace the average Chinese citizen salary will be at 50% of the average US by 2030 (with stock price in Excel provided by Finsheet via Finnhub Stock Api), with the difference being there are 4x more Chinese. Thus a market potential of almost 2x the US over the next decade.

The Southeast Asia Region now contains the largest total number of billionaires, this number is expected to increase at an increasing rate as the region continues to develop. Over the next 10 years the largest trading route ever assembled will be completed, and China will be the primary provider of goods to 5b+ people

2013 North America was home to the largest number of billionaires. This reversed with Asia over the following 5 years. This separation is expected to continue at an increasing rate. Why does this matter? Over the next 10 years the largest trading route ever assembled will be completed, and China will be the primary provider of goods to 5b+ people

Companies that can easily access all customers in the world will perform best. This is good news for Apple, Microsoft, and Disney. Disney stock price in Excel right now is $70. But not for Amazon or Google which at first may sound contrary as the expectation is that Amazon "will take over the world". However one cannot do that without first conquering China. Firms like Alibaba and Tencent will have easy access to the global infrastructure being built by China in an attempt to speed up and ease trade in that region. The following guide shows how to get stock price in Excel.

We will explore companies using a:

1) Past

2) Present (including financial statements)

3) Future

4) Story/Tailwind

Method to find investing ideas in these regions. The tailwind is currently largest in the Asia region with 6%+ GDP growth according to the latest SEC form 4 from Edgar Company Search. This is relevant as investments in this region have a greater margin of safety; investing in a company that maintains flat market share should increase about 6% per year as the market growth size is so significant. The next article I will explore Alibaba (NYSE: BABA), and why I recently purchased a large position during the recent Ant Financial Crisis.

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

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

Initial Jobless Claims in the United States decreased to 236 thousand in the week ending June 21 of 2025 from 246 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

We have successfully extracted a comprehensive news dataset from CNBC, covering not only financial updates but also an extensive range of news categories relevant to diverse audiences in Europe, the US, and the UK. This dataset includes over 500,000 records, meticulously structured in JSON format for seamless integration and analysis.

Diverse News Segments for In-Depth Analysis

This extensive extraction spans multiple segments, such as:

• Business and Market Analysis: Stay updated on major companies, mergers, and acquisitions.
• Technology and Innovation: Explore developments in AI, cybersecurity, and digital transformation.
• Economic Forecasts: Access insights into GDP, employment rates, inflation, and other economic indicators.
• Geopolitical Developments: Understand the impact of political events and global trade dynamics on markets.
• Personal Finance: Learn about saving strategies, investment tips, and real estate trends.

Each record in the dataset is enriched with metadata tags, enabling precise filtering by region, sector, topic, and publication date.

Why Choose This Dataset?

The comprehensive news dataset provides real-time insights into global developments, corporate strategies, leadership changes, and sector-specific trends. Designed for media analysts, research firms, and businesses, it empowers users to perform:

• Trend Analysis
• Sentiment Analysis
• Predictive Modeling

Additionally, the JSON format ensures easy integration with analytics platforms for advanced processing.

Access More News Datasets

Looking for a rich repository of structured news data? Visit our news dataset collection to explore additional offerings tailored to your analysis needs.

Sample Dataset Available

To get a preview, check out the CSV sample of the CNBC economy articles dataset.

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.

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.

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.

Bankruptcy Boom: The U.S. Economy in Turmoil

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

There is substantial evidence that voters’ choices are shaped by assessments of the state of the economy and that these assessments, in turn, are influenced by the news. But how does the economic news track the welfare of different income groups in an era of rising inequality? Whose economy does the news cover? Drawing on a large new dataset of U.S. news content, we demonstrate that the tone of the economic news strongly and disproportionately tracks the fortunes of the richest households, with little sensitivity to income changes among the non-rich. Further, we present evidence that this pro-rich bias emerges not from pro-rich journalistic preferences but, rather, from the interaction of the media’s focus on economic aggregates with structural features of the relationship between economic growth and distribution. The findings yield a novel explanation of distributionally perverse electoral patterns and demonstrate how distributional biases in the economy condition economic accountability.

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

Redbook Index in the United States increased by 4.50 percent in the week ending June 21 of 2025 over the same week in the previous year. This dataset provides the latest reported value for - United States Redbook Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.