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.

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.

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.

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.).

Explore the intricate dance between gold prices and key economic events across major global players – Canada, Japan, USA, Russia, European Union, and China. This comprehensive dataset spans from January 2019 to December 2023, offering a nuanced analysis of how economic news from these influential regions impacts the ever-volatile gold market. Delve into the ebb and flow of financial landscapes, uncovering trends, correlations, and invaluable insights for strategic decision-making in the dynamic world of investments.

Historical Gold Price Dataset:

• Day: The day of the week when the data was recorded.
• Date: The specific date corresponding to the recorded gold price.
• Hour: The time of day when the gold price was recorded.
• Country: The country associated with the economic event or news affecting gold prices.
• Event: The economic event or news that potentially influenced gold prices.
• Actual: The actual reported value or figure related to the economic event.
• Previous: The previously reported value or figure for the same economic event.
• Consensus: The consensus forecast or expected value for the economic event.
• Forecast: The forecasted value or figure for the economic event.

** Economic Calendar Dataset**:

• Day: The day of the week when the economic event is scheduled.
• Date: The specific date when the economic event is expected to occur.
• Hour: The time of day when the economic event is scheduled.
• Country: The country associated with the economic event.
• Event: The specific economic event or news scheduled to take place.
• Actual The actual reported value or figure related to the economic event.
• Previous: The previously reported value or figure for the same economic event.
• Consensus: The consensus forecast or expected value for the economic event.
• Forecast: The forecasted value or figure for the economic event.

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

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.

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, 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 December of 2025.

Get access to leading financial market news coverage including exclusive access to Reuters news as well as 10,500 additional news sources and feeds.

Dataset

This dataset was created by Mayur Jagtap

Released under CC0: Public Domain

Contents

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Trade (EMVMACROTRADE) from Jan 1985 to Nov 2025 about volatility, uncertainty, equity, trade, and USA.

Find unrivaled company, commodity and economic stories formatted for automated consumption, with LSEG Real-Time News, powered by Reuters.

Stay ahead with our comprehensive News Dataset, designed for businesses, analysts, and researchers to track global events, monitor media trends, and extract valuable insights from news sources worldwide.

Dataset Features

News Articles: Access structured news data, including headlines, summaries, full articles, publication dates, and source details. Ideal for media monitoring and sentiment analysis. Publisher & Source Information: Extract details about news publishers, including domain, region, and credibility indicators. Sentiment & Topic Classification: Analyze news sentiment, categorize articles by topic, and track emerging trends in real time. Historical & Real-Time Data: Retrieve historical archives or access continuously updated news feeds for up-to-date insights.

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

Popular Use Cases

Media Monitoring & Reputation Management: Track brand mentions, analyze media coverage, and assess public sentiment. Market & Competitive Intelligence: Monitor industry trends, competitor activity, and emerging market opportunities. AI & Machine Learning Training: Use structured news data to train AI models for sentiment analysis, topic classification, and predictive analytics. Financial & Investment Research: Analyze news impact on stock markets, commodities, and economic indicators. Policy & Risk Analysis: Track regulatory changes, geopolitical events, and crisis developments in real time.

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

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 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.

Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.

The dataset holds 11,932 documents annotated with 3 labels:

sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" }

The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

Economic calendar extracted from Investing.com (CSV) from 01-01-2015 to 31-12-2024 all countries

Columns

• Id of event
• Date (d-m-y)
• Hour of event
• Zone
• Currency
• Importance
• Event
• Actual
• Forecast (prediction of the market)
• Previous

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 contains financial news articles published by HuffPost between 2012 and 2022, curated to support research in financial sentiment analysis, market forecasting, and portfolio optimization. Each entry is formatted in JSON and includes structured fields such as headline, article_link, short_description, author, category, and date_published.

Researchers can leverage this dataset for a wide range of natural language processing (NLP) tasks, including the development and testing of FinBERT and other finance-focused sentiment models. The year-wise separation of the data also facilitates time-series modeling and historical financial trend analyses.

Key Features:

Source: HuffPost financial news articles

Timeframe: 2012–2022

Format: JSON, structured by year

Fields: Headline, link, summary, author, category, publication date

Use Cases:

  Sentiment-informed market prediction

  Event-driven trading strategies

  Portfolio rebalancing based on news sentiment

  Backtesting NLP-driven financial models

Ideal For: Researchers and practitioners in financial engineering, quantitative finance, machine learning, and computational economics.

Licensing: Released under Creative Commons CC0 1.0, making it freely available for both academic and commercial use.