Read the biggest business and political stories from around the world with Reuters Top News, providing a customized experience in an easy-to-use format.

The Reuters-21578 dataset is a collection of documents with news articles. The original corpus has 10,369 documents and a vocabulary of 29,930 words.

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

View Reuters Stocks Buzz through LSEG, providing a sophisticated analysis of equity markets and coverage of hot stocks and sectors.

View Reuters Polls to understand the views of top forecasters in financial markets, and gain polling history of detailed forecasts and consensus estimates.

Thomson Reuters reported $3.49B in Current Assets for its fiscal quarter ending in December of 2024. Data for Thomson Reuters | TRI - Current Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Gain sophisticated commentary on all major economic and business news, including monetary and fiscal policy, M&A, and more with Reuters Breakingviews.

[Keywords] Market include Qlik, Wolters Kluver, H.I.G. Capital, Datasift, TransUnion

Reuters Polls gather insights from experts, presenting the perspectives of leading financial market forecasters at specific moments. These forecasters consist of economists, strategists from both the sell-side and buy-side, independent analysts, and some scholars. The polling archives encompass detailed predictions and consensus estimates for over 900 economic indicators, currency exchange rates, central bank policies on interest rates, money market rates, and bond yields.

Thomson Reuters reported $2.82B in Debt for its fiscal quarter ending in December of 2024. Data for Thomson Reuters | TRI - Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Thomson Reuters reported $28M in Interest Expense on Debt for its fiscal quarter ending in December of 2024. Data for Thomson Reuters | TRI - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Traffic analytics, rankings, and competitive metrics for reuters.com as of May 2025

CLEAR has public record information and is also used for law enforcement and investigations, including personal identification and financial records, police reports, and credential verification services.

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

The Business Information Services market is experiencing robust growth, driven by the increasing need for data-driven decision-making across various industries. The market, estimated at $500 billion in 2025, is projected to maintain a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $850 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the proliferation of big data analytics, and the growing demand for real-time insights. Major players like Bloomberg, Dow Jones, and Thomson Reuters are leveraging advanced technologies like artificial intelligence and machine learning to enhance their offerings and cater to the evolving needs of their clientele. Furthermore, the increasing focus on regulatory compliance across sectors is boosting the demand for comprehensive and reliable business information services. Segmentation within the market includes financial information, market research, credit information, and legal information, each contributing to the overall growth trajectory. However, the market faces certain challenges. Data security concerns and the rising costs associated with data acquisition and maintenance present hurdles for both providers and consumers. Competition is fierce, with established players constantly innovating to maintain their market share and new entrants striving to carve out a niche. The varying regulatory landscapes across different regions also influence market dynamics. Despite these challenges, the long-term outlook for the Business Information Services market remains positive, driven by the continued digital transformation across industries and the ever-increasing reliance on data for strategic decision-making. The market's expansion will likely be propelled by the growing adoption of subscription-based models and the increasing integration of business information services with other enterprise software solutions.

View Reuters FX Buzz to gain actionable insight from commentary on news headlines and deal flow to deep-dive analysis of medium or long-term trends.

Comprehensive legal, tax, public records and business information. Includes Personnet, a service covering all up-to-date laws, rules and regulations specific to human capital. http://legalsolutions.thomsonreuters.com/law-products/ns/government/homeland-security/westlaw

Thomson Reuters reported CAD123.92B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Thomson Reuters | TRI - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

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.

TRI Thomson Reuters Corp Ordinary Shares

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

http://www.daviddlewis.com/resources/testcollections/reuters21578/readme.txt

   Reuters-21578 text categorization test collection
            Distribution 1.0
           README file (v 1.3)
             14 May 2004

            David D. Lewis
      David D. Lewis Consulting and Ornarose, Inc. 
           www.daviddlewis.com

I. Introduction

[Note: There's much that could be improved in this document, but given that Reuters-21578 is being superceded by RCV1, I'm not likely to make those improvements myself. Anyone who would like to create a revised version of this document is invited to contact me.]

This README describes Distribution 1.0 of the Reuters-21578 text categorization test collection, a resource for research in information retrieval, machine learning, and other corpus-based research.

II. Copyright & Notification

The copyright for the text of newswire articles and Reuters annotations in the Reuters-21578 collection resides with Reuters Ltd. Reuters Ltd. and Carnegie Group, Inc. have agreed to allow the free distribution of this data for research purposes only.
If you publish results based on this data set, please acknowledge its use, refer to the data set by the name "Reuters-21578, Distribution 1.0", and inform your readers of the current location of the data set (see "Availability & Questions").

III. Availability & Questions

The Reuters-21578, Distribution 1.0 test collection is available from http://www.daviddlewis.com/resources/testcollections/reuters21578

Besides this README file, the collection consists of 22 data files, an SGML DTD file describing the data file format, and six files describing the categories used to index the data. (See Sections VI and VII for more details.) Some additional files, which are not part of the collection but have been contributed by other researchers as useful resources are also included. All files are available uncompressed, and in addition a single gzipped Unix tar archive of the entire distribution is available as reuters21578.tar.gz.

The text categorization mailing list, DDLBETA, is a good place to send questions about this collection and other text categorization issues. You may join the list by writing David Lewis at ddlbeta-request@daviddlewis.com.

IV. History & Acknowledgements

The documents in the Reuters-21578 collection appeared on the Reuters newswire in 1987. The documents were assembled and indexed with categories by personnel from Reuters Ltd. (Sam Dobbins, Mike Topliss, Steve Weinstein) and Carnegie Group, Inc. (Peggy Andersen, Monica Cellio, Phil Hayes, Laura Knecht, Irene Nirenburg) in 1987.

In 1990, the documents were made available by Reuters and CGI for research purposes to the Information Retrieval Laboratory (W. Bruce Croft, Director) of the Computer and Information Science Department at the University of Massachusetts at Amherst. Formatting of the documents and production of associated data files was done in 1990 by David D. Lewis and Stephen Harding at the Information Retrieval Laboratory.

Further formatting and data file production was done in 1991 and 1992 by David D. Lewis and Peter Shoemaker at the Center for Information and Language Studies, University of Chicago. This version of the data was made available for anonymous FTP as "Reuters-22173, Distribution 1.0" in January 1993. From 1993 through 1996, Distribution 1.0 was hosted at a succession of FTP sites maintained by the Center for Intelligent Information Retrieval (W. Bruce Croft, Director) of the Computer Science Department at the University of Massachusetts at Amherst.

At the ACM SIGIR '96 conference in August, 1996 a group of text categorization researchers discussed how published results on Reuters-22173 could be made more comparable across studies. It was decided that a new version of collection should be produced with less ambiguous formatting, and including documentation carefully spelling out standard methods of using the collection. The opportunity would also be used to correct a variety of typographical and other errors in the categorization and formatting of the collection.

Steve Finch and David D. Lewis did this cleanup of the collection September through November of 1996, relying heavily on Finch's SGML-tagged version of the collection from an earlier study. One result of the re-examination of the collection was the removal of 595 documents which were exact duplicates (based on identity of timestamps down to the second) of other documents in the collection. The new collection therefore has only 21,578 documents, and thus is called the Reuters-21578 collection. This README describes version 1.0 of this new collection, which we refer to as "Reuters-21578, Distribution 1.0".

In preparing the collection...