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Context

The stock and financial market is of great importance to many. News about the stock market can provide an interesting overview of how companies of current events are percieved. With this dataset, you could build a classifier that can differentiate between positive, neutral or bad stock news. Please be aware that this dataset is only meant for educational purposes and does not intent to be investment advice in any way.

Content

The dataset is strucktured as follows: - headline: Headline of an article about stocks or a company - label: Either Positive, Neutral or Negative

Acknowledgements

The stock news were gathered via the website finviz.com.

Inspiration

Are there any errors in this dataset? What would you do with a stock news classifier?

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Context

Scraped from CNBC, the Guardian, and Reuters official websites, the headlines in these datasets reflects the overview of the U.S. economy and stock market every day for the past year to 2 years.

Content

• Data scraped from CNBC contains the headlines, last updated date, and the preview text of articles from the end of December 2017 to July 19th, 2020.
• Data scraped from the Guardian Business contains the headlines and last updated date of articles from the end of December 2017 to July 19th, 2020 since the Guardian Business does not offer preview text.
• Data scraped from Reuters contains the headlines, last updated date, and the preview text of articles from the end of March 2018 to July 19th, 2020.

Inspiration

I firmly believe that the sentiment of financial news articles reflects and directs the performance of the U.S. stock market. Therefore, by applying Natural Language Processing (NLP) through these headlines, I can see how the positivity/negativity of the score through each day correlate to the stock market's gains/losses.

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Thomson Reuters reported $84.49B in Market Capitalization this December 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 December in 2025.

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

Thomson Reuters reported $1.19B in Operating Expenses for its fiscal quarter ending in December of 2024. Data for Thomson Reuters | TRI - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

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...

Thomson Reuters reported $32M in Interest Expense on Debt for its fiscal quarter ending in June of 2025. Data for Thomson Reuters | TRI - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last December in 2025.

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Thomson Reuters reported $4.84 in Dividend Yield for its fiscal quarter ending in June of 2025. Data for Thomson Reuters | TRI - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last December in 2025.

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.

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 December in 2025.

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

Thomson Reuters reported $20M in Stock for its fiscal quarter ending in December of 2023. Data for Thomson Reuters | TRI - Stock including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Reuters-21578 (Text Categorization)

Ruters financial newswire service in 1987

By Huggingface Hub [source]

About this dataset

The Reuters-21578 dataset, one of the most influential and widely used collections of newswire articles from the Reuters financial newswire service, is an essential benchmark for text categorization research. This extensive repository provides a range of valuable insight into topics frequently covered by financial publications and is available in multiple splits for optimal machine learning exploration.

Within this dataset, users will find columns with detailed information such as text (the full body of article text), text_type (classifying whether the article was part of the training or test set), topics (what topics are associated with the particular document), lewis_split (which split it belongs to) , cgis_split (split between train and test set given by core group iteration sampling method), places/people/orgs/exchanges mentioned within it, date and title. In addition to these classifications, there are separate files containing Reuters-21578 articles that were not used in specific splits (ModApte_unused.csv & ModLewis_unused.csv). By leveraging this dataset, you can unlock deep understanding into financial news categorization from an abundance of data points across categories - enabling you to build high performing models that provide better accuracy than ever before!

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How to use the dataset

The Reuters-21578 dataset is a great resource for uncovering valuable insights in financial news. With its wide range of topics and data splits, it is well-suited to be used as a benchmark dataset for text categorization research. Here are some tips and tricks on how to get the most out of this dataset:

1. Familiarize yourself with the columns: Before getting started, make sure to familiarize yourself with all of the columns included in the dataset. This includes understanding what each column means, as well as identifying which are essential for your research project.

2. Use an appropriate split: Depending on your research goals, you may need to use different training and test sets from those provided in this dataset (ModHayes_train/test or ModLewis_train/test). You can also create custom splits from the unique ‘ModApte_unused’ set contained within this collection if desired.

3. Explore other methods: While text categorization is often used with this type of data, you may also want to explore other methods that can help uncover useful information such as topic modelling or sentiment analysis.

4. Leverage related packages: If you’re using Python or R there are some great packages available specifically designed for working with textual data from Reuters-21578 such as sklearn’s reuters21578 module and klabutils’ reutersR package respectively . Both offer helpful features such as vectorizers that let you transform words into feature vectors when implementing ML models such as Naive Bayes or Random Forest classifiers .

5 Tackle low-level preprocessing tasks : Before getting started with building models using ML algorithms , remember that all input data will benefit greatly from being cleaned up first – particularly in terms of removing invalid characters along side any symbols associated with a language other than English; which could severely affect model accuracy! Additionally , performing minor tasks like stopword removal and stemming words into their root form prior to getting underway could help improve overall performance too!

Research Ideas

• Automated text classification - Using the data from the Reuters-21578 dataset, machine learning algorithms can be trained to automatically classify and categorize newswire articles into their appropriate topics. This not only saves time, but also ensures reliable results with minimal human intervention.
• Sentiment analysis - By analyzing the sentiment of individual news article in the Reuters-21578 dataset, one could gain valuable insight into how people generally perceive financial news and then use this information to make more informed investing decisions.
• Stock market predictions - By applying data mining techniques on the content of news articles in this dataset, correlations between certain topics or exchanges mentioned in an article and their effects on stock prices can be identified and used for algorithmic trading strategies aimed at predicting short term stock price movements accurately

Acknowledgements

If you use this dataset in your research, please credit the orig...

Credit-related news headlines as published by Reuters, sourced and kindly permitted for use by the London Stock Exchange Group. Ratings agency credit rating updates have been pruned. Dates: 7th February 2022 - 28th February 2022.

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