The news: The present dataset consists of 1789 news articles from the British daily newspaper The Guardian extracted using the content endpoint of The Guardian Open Platform. The news articles were, at the time, all the news corresponding to the sections: business, politics, society and world news for the entire month of January of 2013 (for a total of 1689 news) and an extra set of news articles randomly selected from the period Febrary of 2013 to December of 2015 (100 news articles). The first set of 1689 news articles was used for training and the second set of 100 news articles was used for testing in two publications: * Maisonnave, M., Delbianco, F., Tohmé, F.A. and Maguitman, A.G., 2018, November. A Supervised Term-Weighting Method and its Application to Variable Extraction from Digital Media. In XIX Simposio Argentino de Inteligencia Artificial (ASAI)-JAIIO 47 (CABA, 2018). * Maisonnave, M., Delbianco, F., Tohmé, F.A. and Maguitman, A.G., 2019. A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval. Inteligencia Artificial, 22(63), pp.61-80. The labels: The entire dataset was manually classified into two possible categories: economically relevant and irrelevant. The labelling process was carried out by two experts in Economy working in collaboration. For each news article, the full text of the article was analyzed to determine the category. The format: There are two different versions for this dataset: the reduced and the full versions. The former consists of a CSV and a readme file. The CSV file has five columns: "Instance No.", "Title", "Web Publication Date", "web URL" and "Economically Relevant". This version is reduced in columns as it does not include the full article texts; however, it does include all the 1789 instances. Requesting the full dataset: To gain access to the full version of the dataset (which includes the body of the news articles), please send an email to mariano.maisonnave@cs.uns.edu.ar with a copy to openplatform@theguardian.com requesting authorization and making it clear that the data set will not be used for commercial purposes.

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.

The Gross Domestic Product (GDP) in the United States contracted 0.50 percent in the first quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.).

China Book, Magazine, Newspaper: Taobao Online Sales: Market Share data was reported at 0.150 % in Aug 2020. This records a decrease from the previous number of 0.180 % for Jul 2020. China Book, Magazine, Newspaper: Taobao Online Sales: Market Share data is updated monthly, averaging 0.150 % from Jun 2019 (Median) to Aug 2020, with 15 observations. The data reached an all-time high of 0.220 % in Feb 2020 and a record low of 0.080 % in Dec 2019. China Book, Magazine, Newspaper: Taobao Online Sales: Market Share data remains active status in CEIC and is reported by Moojing Market Intelligence. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales: Cultural and Entertainment Article.

The Gross Domestic Product (GDP) in the United States expanded 2 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - United States GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Enhancing Financial Market Predictions: Causality-Driven Feature Selection

Note:[Please help give a Like ❤️ if you think this FinSen dataset is good for you, Thanks:)] This paper introduces 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… See the full description on the dataset page: https://huggingface.co/datasets/EagleWHLiang/FinSen.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

This data file contains news intensity measures for the UK and US, based on semantic fingerprints of the news articles from New York Times and the respective country. News articles in the following categories are used: Business Day, New York, U.S., World, Technology, Travel, Health, Real Estate, Science, Education, Automobiles, Your Money, Washington, Climate.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MSCI World: Reflecting Global Economic Trends or Inflated Valuations?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Many predictions of the economic and social costs of a modern-day pandemic are based on the effects of the influenza pandemic of 1918. Despite killing 675,000 people in the United States and 40 million worldwide, the influenza of 1918 has been nearly forgotten. The purpose of this paper is to provide an overview of the influenza pandemic of 1918 in the United States, its economic effects, and its implications for a modern-day pandemic. The paper provides a brief historical background as well as detailed influenza mortality statistics for cities and states, including those in the Eighth Federal Reserve District, that account for differences in race, income, and place of residence. Information is obtained from two sources: (i) newspaper articles published during the pandemic and (ii) a survey of economic research on the subject.

Media outlets provide crucial inputs into the democratic process, yet they face increasingly severe economic challenges. I study how a newly salient manifestation of this pressure, reduced reporting capacity, influences political coverage. Focusing on newspapers in the United States, where industry-wide employment fell over 40% between 2007 and 2015, I use panel data to assess the relationship between reporting capacity and political coverage. Staff cuts substantially decrease the amount of political coverage newspapers provide. Across different samples and measurement approaches, a typical cutback to a newspaper's reporting staff reduces its annual political coverage by between 300 and 500 stories. These political news declines happen against the backdrop of similar reductions in nonpolitical coverage, meaning the share of newspaper articles focused on politics remains stable over this period. This demonstrates that economic pressure affects the political information environment by shaping the media's capacity to cover politics.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset compiles all of the newspaper articles (including news stories, op-eds, and feature stories) on migrant work and migrant workers published in Toronto Star, Globe & Mail, and National Post articles from 2010-2020 (N=1894). This dataset shows the codes ("frames") that articles used to when discussing migrant work and migrant workers.

This repository contains the appendix, the dataset, and the analysis files for the study "Unpacking the Nuances of Agenda-Setting in the Online Media Environment: An Hourly-Event Approach in the Context of Chinese Economic News."Except for the appendix, the "Data" folder contains 36 csv-format files, each for one specific news event. In each file, the first column "hour" denotes hourly intervals of the data, and the 2–6 columns denote the endogenous variables included in the VAR models (i.e., the raw volume of coverage or discussion in different groups concerning media, the neitizens, and other institutions of interest). The datasets have been aggregated by 19-hour lags each day, resulting in 266 lags for the 14-day time window."AnalysisFiles" folder contains the R code and copy results for analysis, in which:-TimeSeriesAnalysis" contains the R code for the time-series analysis of this study. Besides, this folder also contains copies of the results for VAR models.-"t-test & ANOVA" contains the results of 36 separate VAR models and the R code for the t-test and ANOVA for the event feature on the influence of agenda-setting. Besides, this folder also contains copies of the results of t-tests and ANOVA.-"Figure" contains the R code for creating Figure 1 and Figure 2 in the main text of this study and also contains copies of these two figures.