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.

Dataset

This dataset was created by Skywalker

Released under MIT

Contents

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.

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.

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.

Apple Stock (AAPL): Historical Financial News Data is a comprehensive dataset spanning nearly nine years, from February 19, 2016, to November 27, 2024, capturing detailed information about financial news articles related to Apple Inc. (AAPL). It contains 29,752 entries, each representing a unique news article with associated metadata, sentiment analysis, and relevance to Apple stock. The dataset is structured into ten columns, including the publication date of the article in ISO 8601 format, the headline or title of the article, the full content of the news piece, and a URL link to the original article. Each entry also includes stock symbols associated with the content, such as AAPL and other relevant stocks, along with optional tags that provide additional categorization or keywords for the article.

A standout feature of this dataset is its sentiment analysis, which assigns numerical values to quantify the tone of each article. The sentiment polarity score measures the overall sentiment, ranging from negative to positive. Additionally, the sentiment is broken down into three components—negative, neutral, and positive proportions—offering a granular understanding of the article's tone. Sentiment data is available for 29,737 of the articles, enabling in-depth analyses of how news sentiment may correlate with market performance.

This dataset is ideal for analyzing historical trends in financial news sentiment related to Apple stock and exploring the relationship between media coverage and Apple’s stock performance. It can be used to develop natural language processing (NLP) models for financial news sentiment analysis, investigate patterns in stock market reporting, and track the historical coverage and market perception of Apple Inc. With its extensive date range, rich features, and detailed sentiment analysis, this dataset serves as a valuable resource for researchers, investors, and data analysts interested in the intersection of financial news and stock market dynamics.

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.

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.

Dataset

This dataset was created by Saleep Shrestha

Contents

Title: Stock Prices of 500 Biggest Companies by Market Cap (Last 5 Years)

Description: This dataset comprises historical stock market data extracted from Yahoo Finance, spanning a period of five years. It includes daily records of stock performance metrics for the top 500 companies based on market capitalization.

Attributes: 1. Date: The date corresponding to the recorded stock market data. 2. Open: The opening price of the stock on a given date. 3. High: The highest price of the stock reached during the trading day. 4. Low: The lowest price of the stock observed during the trading day. 5. Close: The closing price of the stock on a specific date. 6. Volume: The volume of shares traded on the given date. 7. Dividends: Any dividend payments made by the company on that date (if applicable). 8. Stock Splits: Information regarding any stock splits occurring on that date. 9. Company: Ticker symbol or identifier representing the respective company.

Usefulness: - Investors and analysts can leverage this dataset to conduct various analyses such as trend analysis, volatility assessment, and predictive modeling. - Researchers can explore correlations between stock prices of different companies, sector-wise performance, and market trends over the specified duration. - Machine learning enthusiasts can employ this dataset for developing predictive models for stock price forecasting or anomaly detection.

Note: Prior to using this dataset, it's recommended to perform data cleaning, handling missing values, and verifying the consistency of data across companies and time periods.

License: The dataset is sourced from Yahoo Finance and is provided for analytical purposes. Refer to Yahoo Finance's terms of use for further details on data usage and licensing.

Financial-Leverage-Ratio Time Series for News Corp B. News Corporation, a media and information services company, creates and distributes authoritative and engaging content, and other products and services for consumers and businesses. It operates through five segments: Digital Real Estate Services, Dow Jones, Book Publishing, News Media, and Other. The company distributes content and data products through various media channels, such as newspapers, newswires, websites, mobile apps, newsletters, magazines, proprietary databases, live journalism, video, and podcasts under the MarketWatch, The Wall Street Journal, Barron's, Investor's Business Daily, Factiva, Dow Jones Risk & Compliance, Dow Jones Newswires, and Dow Jones Energy brands. It also owns and operates Monday to Friday, Saturday and Sunday, weekly, and bi-weekly newspapers comprising The Australian, The Weekend Australian, The Daily Telegraph, The Sunday Telegraph, Herald Sun, Sunday Herald Sun, The Courier Mail, The Sunday Mail, The Advertiser, Sunday Mail, The Sun, The Sun on Sunday, The Times, The Sunday Times, and New York Post, as well as digital mastheads and other websites. In addition, the company publishes general fiction, nonfiction, children's, and religious books; and operates Storyful, a social media content agency, as well as sports radio network and news channels. Further, it offers property and property-related advertising and services on its websites and mobile applications; digital real estate services; and financial services. The company has operations in the United States, Canada, Europe, Australasia, and internationally. News Corporation was incorporated in 2012 and is headquartered in New York, New York.

This dataset contains 2000 daily stock market records including price movements, trading volume, market trends, indices, economic scores, and market sentiment information. It covers multiple sectors with a general category column and includes a target column for the next-day closing price. Additional text columns capture market sentiment and news tags for each record. The dataset is designed to provide comprehensive insights into stock market behavior and trends.

Number of Records: 2000

Number of Columns: 18

Column Descriptions:

Category – General text representing the sector or type of stock (e.g., Tech, Finance, Health).

Date – The calendar date of the stock record.

Open – The opening price of the stock on that day.

High – The highest price of the stock during the day.

Low – The lowest price of the stock during the day.

Close – The closing price of the stock on that day.

Volume – The total number of shares traded during the day.

SMA_10 – The 10-day simple moving average of the closing price, showing short-term trend.

EMA_10 – The 10-day exponential moving average of the closing price, giving more weight to recent prices.

Volatility – The standard deviation of the closing price over a 10-day window, representing price fluctuation.

Wavelet_Trend – Trend component of the closing price over a 10-day period.

Wavelet_Noise – Difference between the actual closing price and the trend component, capturing minor fluctuations.

Wavelet_HighFreq – Daily price changes in closing price, showing high-frequency movement.

General_Index – A numeric indicator representing general market performance.

Economic_Score – A numeric score representing overall economic factors impacting the stock.

Market_Sentiment – Text describing the sentiment of the market for that day (Positive, Neutral, Negative).

News_Tag – Text describing the main type of news impacting the stock on that day (e.g., Earnings, Merger).

Close_Next – The closing price of the stock for the next day, serving as the target variable.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

Mega Financial stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The Financial Data category offers insights into the inner workings of financial institutions, hedge funds, and investment companies. This data is gleaned from the website of the world-renowned investment bank, Goldman Sachs. As one of the most respected financial institutions globally, Goldman Sachs is a leading player in the world of high finance, and its website provides a wealth of information on the company's investment strategies, market analysis, and financial news. With a rich history dating back to 1869, Goldman Sachs is known for its expertise in investment banking, securities, and asset management.

Through its website, Goldman Sachs shares its knowledge and expertise on topics such as mergers and acquisitions, equity and fixed income trading, and private wealth management. Financial data enthusiasts can access a treasure trove of information on company performance, market trends, and industry insights, all provided by one of the most respected voices in the financial world. By exploring Goldman Sachs' website, users can gain a deeper understanding of the company's role in shaping the global financial landscape and its impact on the markets.

Incorporating Combinations of Sentiment Scores Of Financial News And MLP-Regressor For Stock Prediction.#

Junaid Maqbool, Preeti Aggarwal, Ravreet Kaur maqbooljunaid@gmail.com

Abstract.

The stock market is very volatile as it depends on political, financial, environmental, and various internal and external factors along with historical stock data. Such information is available to people through microblogs and news and predicting stock price merely on historical data is hard. The high volatility emphasizes the importance to check the effect of external factors on the stock market. In this paper, we have proposed a machine learning model where the financial news is used along with historical stock price data to predict upcoming prices. The paper has used three algorithms to calculate various sentiment scores and used them in different combinations to understand the impact of financial news on stock price as well the impact of each sentiment scoring algorithm. Experiments have been conducted on ten-year historical stock price data as well financial news of four different companies from different sectors to predict next day and next week stock trend and accuracy metrics were checked for a period of 10, 30, and 100 days. Our model was able to achieve the highest accuracy of 0.90 for both trend and future trend when predicted for 10 days. This paper also performs experiments to check which stock is difficult to predict and which stocks are most influenced by financial news and it was found Tata Motors an automobile company stock prediction has maximum MAPE and hence deviates more from actual prediction as compared to others.

Complete research paper can be found at

Incorporating Financial News Sentiments and MLP-Regressor with Feed-Forward for Stock Market Prediction

Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach

Also the pdf of paper is available in the code file as well the data for citation and references

1. Maqbool, Junaid, Preeti Aggarwal, and Ravreet Kaur. "Incorporating Financial News Sentiments and MLP-Regressor with Feed-Forward for Stock Market Prediction." Emerging Technologies for Computing, Communication and Smart Cities: Proceedings of ETCCS 2021. Singapore: Springer Nature Singapore, 2022. 55-67.
2. Maqbool, Junaid, et al. "Stock prediction by integrating sentiment scores of financial news and MLP-regressor: a machine learning approach." Procedia Computer Science 218 (2023): 1067-1078.

Code is publicly available at Github

At Company X, a leading online platform for financial news and analysis, investors and market enthusiasts can access a wealth of information on global market trends, company performances, and economic indicators. With a rich history dating back to the 1960s, the company has established itself as a trusted source for real-time market data, research reports, and in-depth analysis.

From stock prices and trading volumes to financial reports and executive profiles, Company X offers a vast array of data points that provide valuable insights into the inner workings of the financial industry. With a strong focus on accuracy and transparency, the company's data is prized by professionals, researchers, and analysts seeking to stay ahead of the curve in an ever-changing market landscape.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on December of 2025.

Financial Deep Research (FinDeepResearch)

Corporate financial analysis is a critical process for understanding a listed company's business health, financial performance, and stock valuation, ultimately guiding investment decisions. Professional analysts typically execute a comprehensive and rigorous workflow, beginning with the retrieval and recognition of relevant data from diverse sources, such as corporate disclosures, financial news, historical stock prices, and market indexes.… See the full description on the dataset page: https://huggingface.co/datasets/OpenFinArena/FinDeepResearch.

Price-To-Tangible-Book-Ratio Time Series for News Corp A. News Corporation, a media and information services company, creates and distributes authoritative and engaging content, and other products and services for consumers and businesses. It operates through five segments: Digital Real Estate Services, Dow Jones, Book Publishing, News Media, and Other. The company distributes content and data products through various media channels, such as newspapers, newswires, websites, mobile apps, newsletters, magazines, proprietary databases, live journalism, video, and podcasts under the MarketWatch, The Wall Street Journal, Barron's, Investor's Business Daily, Factiva, Dow Jones Risk & Compliance, Dow Jones Newswires, and Dow Jones Energy brands. It also owns and operates Monday to Friday, Saturday and Sunday, weekly, and bi-weekly newspapers comprising The Australian, The Weekend Australian, The Daily Telegraph, The Sunday Telegraph, Herald Sun, Sunday Herald Sun, The Courier Mail, The Sunday Mail, The Advertiser, Sunday Mail, The Sun, The Sun on Sunday, The Times, The Sunday Times, and New York Post, as well as digital mastheads and other websites. In addition, the company publishes general fiction, nonfiction, children's, and religious books; and operates Storyful, a social media content agency, as well as sports radio network and news channels. Further, it offers property and property-related advertising and services on its websites and mobile applications; digital real estate services; and financial services. The company has operations in the United States, Canada, Europe, Australasia, and internationally. News Corporation was incorporated in 2012 and is headquartered in New York, New York.