The main stock market index of United States, the US500, rose to 7152 points on April 29, 2026, gaining 0.18% from the previous session. Over the past month, the index has climbed 12.74% and is up 28.42% compared to the same time last year, 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 April of 2026.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this April 27 of 2026.

To address the issues of "logical inconsistencies in synthetic data" or "outdated data" present in existing publicly available financial datasets, this project has constructed a high-quality dataset based on real-world data. This dataset utilizes an ETL pipeline to rigorously align unstructured news text with structured market trading data in terms of time, aiming to provide a reliable benchmark for financial NLP (Natural Language Processing) and quantitative predictive analysis.

The dataset contains over 2500 records, covering 115 major US blue-chip stocks (including technology, finance, healthcare, and consumer sectors). Each row represents an independent news event and its corresponding market reaction on that day.

US Stock News, offered by Benzinga, is the gateway to over 200 full-length stories and 1000 original content pieces created daily by an in-house editorial team. News events cover everything from M&A deals to Federal Reserve announcements.

A decisive advantage of this data feed is its structural format. REST API lets you filter news by date, company ticker, CIK, ISIN, and other identifiers. Response contains the text URL, image URL, tags, author, title, and timestamps. In addition to the API, news can be accessed via spreadsheet add-ons.

The primary price indicator for companies is the number of users who will be using or seeing earnings data. Individual, non-commercial users can always choose 0. No agreements or licenses are required to be signed. Finazon partnered with Benzinga to provide lower rates and let users enjoy the marketplace's synergy.

Lokeshnathy/Stock-Market-News-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

The main stock market index of United States, the US500, fell to 7158 points on April 27, 2026, losing 0.09% from the previous session. Over the past month, the index has climbed 12.84% and is up 29.48% compared to the same time last year, 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 April of 2026.

Multi-Source Financial & General News

🚀 57.1 MILLION ROWS OF NEWS CONTENT — one unified corpus for market-aware AI/ML

I combined 24 public news datasets (many small on their own) into one consistent, ready-to-use layer so you don’t have to wrangle them yourself. Everything is normalized to a minimal schema (date, text, extra_fields) and shipped as Parquet shards per subset—streamable, DuckDB-friendly, and built with a trading date policy (this can be edited if folks see other use… See the full description on the dataset page: https://huggingface.co/datasets/Brianferrell787/financial-news-multisource.

Prices for United States Stock Market Index (US1000) including live quotes, historical charts and news. United States Stock Market Index (US1000) was last updated by Trading Economics this April 28 of 2026.

US Stock News With Price

A stock news and price dataset design for the alignment between financial news and the market. Please only use for academic purpose.

  1. Data Description

date: The date of the news published. stock: The symbol of the stocks the news related to. (checked by whether title or content has the company information. title: The title of the news. content: The content of the news. trading_date: Here is the assumed trading date, which should be the… See the full description on the dataset page: https://huggingface.co/datasets/oliverwang15/us_stock_news_with_price.

FNSPID: A Comprehensive Financial News Dataset in Time Series

  Description

FNSPID is a meticulously curated dataset designed to support research and applications in the field of financial news analysis within the context of time-series forecasting. Our dataset encompasses a wide range of financial news articles, providing a rich resource for developing and testing models aimed at understanding market trends, investor sentiment, and other critical financial indicators. Link… See the full description on the dataset page: https://huggingface.co/datasets/Zihan1004/FNSPID.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Trade (EMVMACROTRADE) from Jan 1985 to Mar 2026 about macro, volatility, uncertainty, equity, trade, and USA.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates (EMVMACROINTEREST) from Jan 1985 to Mar 2026 about macro, volatility, uncertainty, equity, interest rate, interest, rate, and USA.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Consumer Spending And Sentiment (EMVMACROCONSUME) from Jan 1985 to Feb 2026 about macro, volatility, uncertainty, equity, PCE, consumption expenditures, consumption, personal, and USA.

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.

Context

"Greed, for lack of a better word, is good"

The stock market has always intrigued me. Numbers, Charts, High-pressure environments, thinking on your feet, Wall Street, Hedge funds all of it!

To even begin to explore stocks, one needs to have a good amount of historical data and knowledge of a lot of technical details. Now, OHLC(Open High Low Close) data is available easily on many websites nowadays. But those as features are not enough to predict the stock prices. The stock market depends upon many many factors such as previous days performance, Global financial news, Public sentiment about the company, Mergers & Acquisitions, Moving Averages, etc

Feature Extraction is a tedious job to do, more so when we are talking about stocks. I have created this Pipeline to extract many Technical Indicators as well as lagged features for training Machine Learning algorithms for forecasting Stock Prices. One can also train multiple algorithms on multiple stocks and get an evaluation instantly on how did it perform.

Please check out the app below- https://stock-prediction-dashboard.herokuapp.com/

High-quality financial data especially which requires domain knowledge & expertise in quantitative methods is difficult to get and even if available it would be very costly. This was my motivation for creating and uploading this dataset on Kaggle, so anyone can leverage these extracted features and indicators to build and train their own machine learning models and identify patterns & trends.

Content

This dataset has around 64 features which include features extracted from OHLC, other index prices such as QQQ(Nasdaq-100 ETF) & S&P 500, technical indicators such as Bollinger bands, EMA(Exponential Moving Averages), Stochastic %K oscillator, RSI, etc.

Furthermore, It has lagged features from previous day price data as we know previous day prices affect the future stock price. Then, the data has date features which specify, if it's a leap year, if its month start or end, Quarter start or end, etc.

All of these features have something to offer for forecasting. Some tell us about the trend, some give us a signal if the stock is overbought or oversold, some portrays the strength of the price trend.

I will keep on adding all of the Nasdaq-100 companies to the dataset for the past 10 years approx. So when completed, this data will contain around 100 stocks.

Acknowledgements

This dataset belongs to me. I’m sharing it here so that people can build upon it and try and create some effective methods to predict the random walk.

Inspiration

Can you predict the unpredictable? Can you predict the Stock market movement using machine learning or deep learning techniques? To be precise, Can you generate realistic buy/sell signals for the next day based on future stock price estimates using time series modeling?

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Real Estate Markets (EMVMACRORE) from Jan 1985 to Feb 2026 about macro, volatility, uncertainty, equity, real estate, and USA.

Supplementary information files for the article Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises

Abstract: This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Inflation (EMVMACROINFLATION) from Jan 1985 to Mar 2026 about macro, volatility, uncertainty, equity, inflation, and USA.

Daily News for Stock Market Prediction

Using 8 years daily news headlines to predict stock market movement

  Dataset Info

Source: Kaggle Original Size: 5.82 MB Kaggle Downloads: 63,510 Files: 3

  Files

Combined_News_DJIA.csv RedditNews.csv upload_DJIA_table.csv

Mirrored from Kaggle

Key Takeaways

• Market Size: Expected to grow from USD 2,205.1 billion in 2024 to USD 18,201 billion by 2034.
• CAGR: 23.50% during the forecast period.
• Dominant Region: Asia-Pacific leads with 40% market share.
• Leading Segment: Electronics & Media (30% market share).
• Top Segment: B2C dominates with 71% market share.
• Popular Payment Method: Credit/Debit Cards (40% market share).

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