CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Who Benefits?

• Researchers & Academics studying macro-economics or financial policy.
  
• Data Scientists building AI models, trend analyzers, or economic forecasting tools.
  
• Economists & Analysts need real-world news data for policy analysis.
  
• Content Strategists who write data-backed articles about economic trends.

Why Choose This Dataset?

• No need to manually scrape CNBC — data is pre-extracted and clean.
  
• High-quality economy news metadata enables detailed filtering (by date, author, topic).
  
• Ready for machine learning, sentiment analysis, or building news-based economic models.
  
• Well-suited for trend tracking, policy analysis, and economic forecasting.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

Stay informed with our comprehensive Financial News Dataset, designed for investors, analysts, and businesses to track market trends, monitor financial events, and make data-driven decisions.

Dataset Features

Financial News Articles: Access structured financial news data, including headlines, summaries, full articles, publication dates, and source details. Market & Economic Indicators: Track financial reports, stock market updates, economic forecasts, and corporate earnings announcements. Sentiment & Trend Analysis: Analyze news sentiment, categorize articles by financial topics, and monitor emerging trends in global markets. Historical & Real-Time Data: Retrieve historical financial news archives or access continuously updated feeds for real-time insights.

Customizable Subsets for Specific Needs Our Financial News Dataset is fully customizable, allowing you to filter data based on publication date, region, financial topics, sentiment, or specific news sources. Whether you need broad coverage for market research or focused data for investment analysis, we tailor the dataset to your needs.

Popular Use Cases

Investment Strategy & Risk Management: Monitor financial news to assess market risks, identify investment opportunities, and optimize trading strategies. Market & Competitive Intelligence: Track industry trends, competitor financial performance, and economic developments. AI & Machine Learning Training: Use structured financial news data to train AI models for sentiment analysis, stock prediction, and automated trading. Regulatory & Compliance Monitoring: Stay updated on financial regulations, policy changes, and corporate governance news. Economic Research & Forecasting: Analyze financial news trends to predict economic shifts and market movements.

Whether you're tracking stock market trends, analyzing financial sentiment, or training AI models, our Financial News Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Enhancing Financial Market Predictions: Causality-Driven Feature Selection FinSen dataset that revolutionizes financial market analysis by integrating economic and financial news articles from 197 countries with stock market data. The dataset’s extensive coverage spans 15 years from 2007 to 2023 with temporal information, offering a rich, global perspective 160,000 records on financial market news. Our study leverages causally validated sentiment scores and LSTM models to enhance market forecast accuracy and reliability.

ABSTRACT The conventional view on the U.S. economy is that economic growth above “potential” is bad for bonds since it spells inflation. The purpose of this note is to show that following the Volker deflation (l980-82), the policy regime changed, and greater economic stability obtained.

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.

Explore the "Bloomberg Quint News Dataset," a comprehensive collection of news articles from Bloomberg Quint, a leading source of financial, business, and economic news in India and around the world.

This dataset includes thousands of articles covering a wide range of topics, such as financial markets, economic policies, corporate news, technology, politics, and more. Each article in the dataset comes with detailed information, including headlines, publication dates, authors, article content, and categories, offering valuable insights for researchers, data analysts, and media professionals.

Key Features:

• Extensive Coverage: Thousands of news articles from Bloomberg Quint, covering diverse topics including business, finance, economics, technology, and global news.
• Detailed Metadata: Each article includes key details such as headline, publication date, author, content, and category, making it ideal for in-depth research and analysis.
• Ideal for Analysis: Perfect for researchers, data scientists, and content strategists looking to analyze trends in news reporting, study media coverage, or develop content strategies.
• Rich Source of Information: Provides up-to-date information on financial markets, economic policies, and global events, helping professionals stay informed and make data-driven decisions.

Whether you're researching financial trends, analyzing media coverage, or developing new content, the "Bloomberg Quint News Dataset" is an invaluable resource that offers detailed insights and extensive coverage of the latest news.

China’s Protocol of Accession to the World Trade Organization, signed on December 2001, allowed other country members to consider China as a Non-Market Economy (NME) until the end of 2016. The aim of this article is to answer the following question: Can the Market Economy Status (MES) Recognition be measured in its compliance? The proxy used for that compliance was the number of antidumping investigations initiated per country. The expectation is that countries recognizing Chinese MES would initiate fewer antidumping investigations than countries still treating China as a NME. This would explain why the Chinese government has been campaigning vigorously since 2001 to gain MES among its economic partners. Using count-models, we demonstrate that MES had a positive impact in reducing the number of antidumping investigations against Chinese products.

Note: Updates to this data product are discontinued. County boundaries do not always accurately define local economies. Commuting zones and Labor Market Areas combine counties into units intended to more closely reflect the geographic interrelationships between employers and labor supply.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data download page For complete information, please visit https://data.gov.

Supplementary information files for the article Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises

Abstract: This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

Explore the largest pre-crawled news articles dataset from CNBC, a leading global news source for business, finance and current affairs. This comprehensive news dataset includes thousands of articles covering a wide range of topics: financial markets, economic trends, technology, politics, health, and more. Each entry in this dataset provides detailed information, including headlines, publish dates, authors, article content and categories — offering valuable insights for researchers, data analysts and media professionals.

Key Features

• Extensive Coverage: Thousands of news articles from CNBC, spanning business, finance, technology and global news.
  
  
• Detailed Metadata: Each news record includes headline, publication date, author, content and category — enabling in-depth research.
  
  
• Ready for Analysis: Ideal for data scientists, content strategists and media researchers building trend-analysis, media-monitoring or news-analytics models.
  
  
• Immediate Use: Data is delivered in CSV format, instantly downloadable, ready for machine-learning, AI modelling or reporting pipelines.
  

Who benefits?

• Academic researchers analysing media coverage or news sentiment.
  
  
• Data scientists building news analytics dashboards or training NLP models.
  
  
• Content teams / SEO specialists sourcing datasets to support articles about media trends or data-driven stories.
  
  
• Global marketers investigating business & financial news trends across the US/UK/EU markets.
  

Why choose this dataset?

• Ready to use: no manual scraping required.
  
  
• High-quality metadata allowing filtered exports by topic, date or author.
  
  
• Broad topic coverage: business, finance, technology, politics, health, and global news.
  

Ideal for building derivatives: summarisation, classification, clustering.

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information.

Abstract of associated article: In emerging-market economies, real exchange rate adjustment is critical for achieving a sustainable current account position and thereby for helping to maintain macroeconomic and financial stability. This study examines two related hypotheses: (i) that real exchange rate adjustment promotes the rebalancing of the current account and (ii) that a flexible nominal exchange rate facilitates real exchange rate adjustment and thus the rebalancing of the current account. Evidence from an event-study analysis for a large set of emerging-market economies over the period 1975–2008 indicates that real exchange rate adjustment has contributed significantly to reducing current account imbalances. The adjustment of current account deficits in countries with a fixed exchange rate regime typically occurs through an exchange rate crisis, and substantial costs in terms of forgone output are incurred. Vector-error-correction analysis supports the findings of the event study; namely, in the long run, real exchange rate movements facilitate current account adjustment.

Abstract The paper focuses on the manifestations of financialization in the international sphere, which it defines as the increasing magnitude of finance and its decoupling from earlier functions and logic as the speculative motive is strengthened. With financialization the motive of finance is no longer to finance trade and production but to accumulate wealth, which in emerging market economies (EMEs) takes place through innovative products and practices that have in common the focus on exchange rate returns, resulting in a strengthened speculative motive. The article reviews the financialization literature highlighting how the different closed-economy aspects impact the international sphere. It conducts empirical analyses based on the financial integration of a country and on the characteristics of its currencies’ FX markets to assess the presence of financialization and its characteristics among EMEs, indicating certain countries where this process is more intense.

These charts provide a snapshot of the domestic and global market for rice, the primary staple for more than half the world's population. Excel files are available from the monthly Outlook reports.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Chart gallery For complete information, please visit https://data.gov.

Information reports on agricultural situations in more than 130 countries submitted by overseas offices of USDA's Foreign Agricultural ServiceThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web Page For complete information, please visit https://data.gov.

China Commodity Trading Market over 100 M Yuan: Number of Market: Daily Use Articles and Cultural Goods Market data was reported at 60.000 Unit in 2023. This records a decrease from the previous number of 61.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Market: Daily Use Articles and Cultural Goods Market data is updated yearly, averaging 88.000 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 105.000 Unit in 2010 and a record low of 60.000 Unit in 2023. China Commodity Trading Market over 100 M Yuan: Number of Market: Daily Use Articles and Cultural Goods Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Market.

This is an agent-based computer model of the labor market. This is mainly used to study the impact of expanding the scope of workers'job search. This model includes several subroutines, such as job search, wage negotiation and variable update. This model needs to be run in Netlogo program. Netlogo is a software for multi-agent simulation modeling. Software download link: http://ccl.northwestern.edu/netlogo/

This data file contains the annual weights and returns of the global invested multi-asset market portfolio of Doeswijk, Lam, and Swinkels (2019) "Historical returns of the market portfolio" Review of Asset Pricing Studies

Interview notes and transcripts from fieldwork conducted in China in 2015, 2016, and 2018.

These files are related to the published paper "Why do farmers' cooperatives fail in a market economy? Rediscovering Chayanov with the Chinese experience".

A monthly and quarterly data set spanning July 1995 to December 2016 of the following macro-economic variables 1. South African stock market 2. South African GDP3. United States GDP 4. South African interest rate 5. US interest rate 6. South African inflation rate 7. US inflation rate 8. South African Money Supply 9. Rand/Dollar Exchange 10. FTSE