📝 Description

This dataset contains over 400,000 macroeconomic events collected from global sources across more than 90 countries and regions, covering years 2020–2025. It mirrors professional economic calendars used by traders, economists, and analysts to track key economic indicators that move financial markets.

Each event includes its scheduled release time, geographical zone, currency, importance level, and actual, forecast, and previous values when available.

You can use this dataset for:

• 📈 Economic forecasting and sentiment analysis
• 💹 Financial market event-driven modeling
• 🧠 Machine learning applications on macroeconomic signals
• 🌍 Comparative studies of country-level indicators

📊 Dataset Structure

🌐 Coverage

• Zones: 90+ economies — including United States, Euro Zone, China, Japan, United Kingdom, India, Brazil, Australia, Türkiye, South Africa, and more.
• Currencies: USD, EUR, JPY, GBP, CNY, INR, and 50+ others.
• Time Period: ~2000 to 2025 (depending on data availability).
• Events Count: 409,234 records.

⚙️ Data Quality Notes

• Missing values in currency, importance, or actual columns occur mainly for minor or regional events.
• Times are reported as given in the source; some may represent local times.
• The dataset is cleaned and standardized for country and currency fields but can be further enriched with ISO country codes or UTC timestamps.

🧠 Example Use Cases

1. Market Volatility Forecasting: Use event importance and actual-vs-forecast deviations to predict short-term asset volatility.
2. Macroeconomic Trends: Track inflation or employment releases over time by country or region.
3. Event Sentiment Modeling: NLP on the event column for topic clustering (e.g., inflation vs. housing).
4. Calendar Effect Studies: Combine with asset price data (SPX, EURUSD, etc.) to measure event-driven reactions.

📦 File Info

• economic_calendar.csv

  • 409,234 rows
  • 10 columns
  • Size: ~31 MB

🏁 Tags

economics, macroeconomics, finance, forex, stock-market, forecasting, time-series, machine-learning, econometrics

🔗 Suggested License

If it’s scraped or aggregated from public calendars (like Investing.com), use: CC BY-NC-SA 4.0 — Attribution-NonCommercial-ShareAlike.

Financial News Events Dataset - Comprehensive Description

Overview

This synthetic dataset contains 3,024 records of financial news headlines centered around major market events from February 2025 to August 2025. The dataset captures real-time market dynamics, sentiment analysis, and trading patterns across global financial markets, making it ideal for financial analysis, sentiment modeling, and market prediction tasks.

Dataset Specifications

• Total Records: 3,024 rows
• Total Features: 12 columns
• Date Range: February 1, 2025 - August 14, 2025
• File Formats: CSV, JSON, XLSX
• Data Quality: ~5% null values strategically distributed for realistic data cleaning scenarios

Column Descriptions

Key Features & Statistics

Market Events Coverage (20 Categories)

• Stock Market Crashes & Rallies
• Interest Rate Changes & Central Bank Meetings
• Corporate Earnings Reports & IPO Launches
• Government Policy Announcements
• Trade Tariffs & Geopolitical Events
• Cryptocurrency Regulations
• Supply Chain Disruptions
• Economic Data Releases

Global Market Indices (18 Major Indices)

• US Markets: S&P 500, Dow Jones, Nasdaq Composite, Russell 2000
• Indian Markets: NSE Nifty, BSE Sensex
• European Markets: FTSE 100, DAX, Euro Stoxx 50, CAC 40
• Asian Markets: Nikkei 225, Hang Seng, Shanghai Composite, KOSPI
• Others: TSX, ASX 200, IBOVESPA, S&P/TSX Composite

News Sources (18 Reputable Publications)

Major financial news outlets including Reuters, Bloomberg, CNBC, Financial Times, Wall Street Journal, Economic Times, Forbes, and specialized financial publications.

Sector Distribution (18 Business Sectors)

Technology, Finance, Healthcare, Energy, Consumer Goods, Utilities, Industrials, Materials, Real Estate, Telecommunications, Automotive, Retail, Pharmaceuticals, Aerospace & Defense, Agriculture, Transportation, Media & Entertainment, Construction.

Data Quality & Preprocessing Notes

• Realistic Null Distribution: Approximately 5% null values in key columns (Headline, Sentiment, Index_Change_Percent, News_Url) to simulate real-world data collection challenges
• Balanced Sentiment Distribution: Mix of positive, neutral, and negative sentiment classifications
• Diverse Market Conditions: Index changes ranging from -5% to +5% reflecting various market scenarios
• Volume Variability: Trading volumes span 1M to 500M to represent different market liquidity conditions

Potential Use Cases

📈 Financial Analysis

• Market sentiment analysis and trend prediction
• Correlation studies between news events and market movements
• Trading volume pattern analysis

🤖 Machine Learning Applications

• Sentiment classification model training
• Market movement prediction algorithms
• News headline generation models
• Event-driven trading strategy development

📊 Data Visualization Projects

• Interactive market sentiment dashboards
• Time-series analysis of market events
• Geographic distribution of financial news impact
• Sector-wise performance visualization

🔍 Research Applications

• Academic research on market efficiency
• News impact analysis on different sectors
• Cross-market correlation studies
• Event study methodologies

Technical Specifications

• Memory Usage: Approximately 1.5MB across all formats
• **Proces...

This dataset aggregates real-time sentiment scores and metadata for financial news headlines, enabling rapid detection of market-moving events and trends. It includes headline text, publication details, sentiment analysis, relevance to financial markets, and links to affected stocks and sectors. Ideal for quantitative trading, risk monitoring, and financial news analytics.

This dataset is designed to support research and model development in financial market forecasting. It consists of daily stock market data for multiple companies, enriched with macroeconomic indicators and simulated market stress events to reflect real-world volatility.

Key features include:

Stock price details (Open, High, Low, Close) and Trading Volume

Macroeconomic indicators such as GDP growth rate, inflation rate, interest rate, and unemployment rate

A Market Stress Level (normalized between 0 and 1) indicating systemic volatility

A binary Event Flag to simulate major financial shocks or critical economic events

Data spans across multiple tickers (e.g., AAPL, GOOGL, TSLA) for 500+ trading days

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.

Financial news significantly influences investment decisions, stock market trends, and corporate strategies. However, extracting meaningful insights from unstructured news articles, particularly event-cause relationships, remains a challenge. This dataset addresses this gap by providing manually annotated event-cause pairs from financial news, enabling improved predictive modeling, risk assessment, and automated trading strategies.

Dataset Composition:

The dataset comprises 456 financial news articles from the following four major Indian financial news sources.

Business Standard

Economic Times

Live Mint

Moneycontrol

It covers articles from 2021 to 2025. Each entry includes annotated event-cause relationships along with metadata such as stock symbols, stock change, company names, and financial indicators. The dataset categorizes events into five key types:

Financial Reports & Earnings Announcements

Mergers & Acquisitions

Regulatory Changes & Legal Actions

Executive Leadership Changes

Market & Economic Trends

Dataset Attributes

The dataset comprises the following attributes:

Source: The origin of the news article (e.g., financial news websites).

Title: The headline of the article.

Content: The full text of the article.

Date: The publication date of the article.

Stock: Name of the Stock.

Labels: The annotation Tags (e.g., ORG, EVENT, CAUSE)

Stock Gain/Loss Percent: The percentage change in stock price associated with the event described in the article. The gain/loss percent was manually extracted from the Tickertape website.

The dataset is structured in JSON format and CSV, ensuring efficient storage and accessibility.

Applications:

This dataset supports event-cause extraction in financial NLP applications such as:

Stock market prediction using causal analysis

Algorithmic trading models incorporating financial event impact

Sentiment analysis & risk assessment for investment strategies

Corporate strategy evaluation based on financial event insights

The Economic Indicator Service (EIS) aims to deliver economic content to financial institutions on both buy and sell-side and service providers. This new service currently covers 34,351 recurring macro-economic indicators from 135 countries ( as of December 16, 2019 ) such as GDP data, unemployment releases, PMI numbers etc.

Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.

Depending on availability, information regarding economic indicators, including the details of the issuing agency as well as historical data series can be made accessible for the client. Key information about EIS: • Cloud-based service for Live Calendar – delivered via HTML/JavaScript application formats, which can then be embedded onto any website using iFrames • Alternatives methods available – such as API and JSON feed for the economic calendar that can be integrated into the company’s system • Live data – updated 24/5, immediately after the data has been released • Historical data – includes a feed of all previous economic indicators available We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. The calendar includes the following. • Recurring & Non-recurring indicators covering 136 countries across 21 regions. • Indicators showing high, medium, and low impact data. • Indicators showing actual, previous, and forecast data. • Indicators can be filtered across 16 subtypes. • News generation for selected high-impact data. • Indicator description and historical data up to the latest eight historical points with a chart.

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.).

A real-time data feed of scheduled global economic events embedded via Finlogix, including releases like CPI, FOMC decisions, and NFP.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

Stock Market Data: 9,000+ Tickers (1962 - Present)

Dataset Overview

This dataset offers comprehensive historical stock market data covering over 9,000 tickers from 1962 to the present day. It includes essential daily trading information, making it suitable for various financial analyses, trend studies, and algorithmic trading model development.

Columns

• Date: The date of the recorded trading data.
• Ticker: The stock symbol of the company.
• Open: Opening price of the stock on the trading day.
• High: Highest price reached during the trading day.
• Low: Lowest price reached during the trading day.
• Close: Closing price of the stock on the trading day.
• Volume: The total number of shares traded during the day.
• Dividends: Cash dividends issued on the date, if applicable.
• Stock Splits: Stock split factor for the date, if any split occurred.

Usage

This dataset is ideal for: - Time-Series Analysis: Track stock price trends over time, examining daily, monthly, and yearly patterns across sectors. - Algorithmic Trading: Develop and backtest trading strategies using historical price movements and volume data. - Machine Learning Applications: Train models for stock price prediction, volatility forecasting, or portfolio optimization. - Quantitative Research: Perform event studies, analyze the impact of dividends and stock splits, and assess long-term investment strategies. - Comparative Analysis: Evaluate performance across industries or against broader market trends by analyzing multiple tickers in one dataset.

This dataset serves as a robust resource for academic research, quantitative finance studies, and financial technology development.

Real-time economic events including Fed decisions, GDP reports, employment data, and inflation indicators

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

This dataset contains 4,987 daily record behavior of financial markets. It includes stock price metrics, macroeconomic indicators, sentiment scores, and event flags.

Key highlights:

Time span: 4,987 days

Financial indicators: Open, High, Low, Close, Adjusted Close, Volume

Macroeconomic variables: GDP, Inflation, Unemployment, Interest Rate, CPI

Sentiment analysis: News and Social Sentiment scores

Event tagging: Binary event flag (e.g., market shocks)

Target label: Market condition — Stable, Volatile, or Crash

US Stock News, offered by Benzinga, is the gateway to over 200 full-length stories and 1000 original content pieces created daily by an in-house editorial team. News events cover everything from M&A deals to Federal Reserve announcements.

A decisive advantage of this data feed is its structural format. REST API lets you filter news by date, company ticker, CIK, ISIN, and other identifiers. Response contains the text URL, image URL, tags, author, title, and timestamps. In addition to the API, news can be accessed via spreadsheet add-ons.

The primary price indicator for companies is the number of users who will be using or seeing earnings data. Individual, non-commercial users can always choose 0. No agreements or licenses are required to be signed. Finazon partnered with Benzinga to provide lower rates and let users enjoy the marketplace's synergy.

The Dow Jones Industrial Average (DJIA) is a stock market index used to analyze trends in the stock market. While many economists prefer to use other, market-weighted indices (the DJIA is price-weighted) as they are perceived to be more representative of the overall market, the Dow Jones remains one of the most commonly-used indices today, and its longevity allows for historical events and long-term trends to be analyzed over extended periods of time. Average changes in yearly closing prices, for example, shows how markets developed year on year. Figures were more sporadic in early years, but the impact of major events can be observed throughout. For example, the occasions where a decrease of more than 25 percent was observed each coincided with a major recession; these include the Post-WWI Recession in 1920, the Great Depression in 1929, the Recession of 1937-38, the 1973-75 Recession, and the Great Recession in 2008.

In this table, we list major worldwide stock market crashes from 2007 to 2023. For each crash, we show its name, rough time of occurrence, stock index’s high and low, and in which country it occurred.

Abstract Purpose: The objective of this study is to analyze the impact of changes in credit ratings on the long-term return of Brazilian firms. Design/methodology/approach: We conducted an event study to measure how stock prices in the Brazilian stock exchange (B3) react to rating upgrades and downgrades by Moody’s and S&P. Findings: Our sample presents positive and significant returns measured by the BHAR for ratings downgrades and non-significant ones for upgrades. Our data also show the important role of the previous rating in explaining these results in a non-linear fashion. Originality/value: Our research makes an important contribution to the theory of market efficiency, analyzing the degree of information present in the announcements of credit ratings changes. We also present results for Brazilian companies, correcting gaps pointed out in previous methodologies.

content

This dataset records world economic events on a calendar basis. Date, time, country/zone, currency, event name, importance level, and actual/predicted/previous economic values (if available) are among the details that are included in each row, which represents a single event. Columns such as id, date, time, zone, currency, importance, event, actual, forecast, and prior are included in the dataset. These areas aid in monitoring market-moving announcements, national public events and holidays, and economic indicators.

context

Financial analysis, forecasting, and comprehending the impact of world events on markets and currencies may all be done with this dataset. These economic calendars are used by traders, economists, and data analysts to examine how significant announcements (such as interest rates, inflation figures, and holidays) affect market activity. Time-series forecasting models, market reaction studies, and EDA initiatives that investigate the connections between financial patterns and economic events can all benefit from its support.

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.