The main stock market index of United States, the US500, fell to 6238 points on July 4, 2025, losing 0.65% from the previous session. Over the past month, the index has climbed 5.04% and is up 12.06% 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 July of 2025.

This dataset features financial news headlines collected from leading financial news websites, including CNBC, The Guardian, and Reuters. It provides an overview of the U.S. economy and stock market, primarily reflecting daily market sentiment over several years. The main purpose of this dataset is to facilitate Natural Language Processing (NLP) analyses to explore the correlation between the positivity or negativity of news sentiment and U.S. stock market performance, such as gains and losses. It is ideal for data scientists and analysts keen on understanding market dynamics through textual data.

Columns

The dataset typically includes the following columns, though availability may vary slightly by source: * Headlines: The main title or headline of the financial article. * Time: The last updated date and time of the article. * Description: A preview or summary text of the article's content.

Distribution

The data files are generally provided in CSV format. Specific numbers for rows or records are not available within the provided sources, but the dataset is structured to allow for easy processing and analysis.

Usage

This dataset is well-suited for a variety of applications, including: * Sentiment analysis of financial news to predict market movements. * Developing and testing Natural Language Processing (NLP) models. * Data science and analytics projects focused on economic trends and stock market performance. * Research into the impact of media on financial markets.

Coverage

The dataset covers news related to the U.S. economy and stock market. * Time Range: * CNBC and The Guardian data spans from late December 2017 to 19th July 2020. * Reuters data covers from late March 2018 to 19th July 2020. * Collectively, the headlines reflect an overview of the U.S. economy and stock market for approximately one to two years from their scraping date.

License

CCO

Who Can Use It

This dataset is intended for a range of users, including: * Data Scientists and Analysts performing market sentiment analysis. * Researchers studying economic indicators and financial news impact. * Individuals interested in Natural Language Processing (NLP) and text analysis applications in finance. * Anyone looking to gain insights into the relationship between news sentiment and stock market performance.

Dataset Name Suggestions

• US Financial News Headlines
• Stock Market Sentiment News
• Financial Article Headlines
• Daily Market News Dataset
• Economy News Headlines for NLP

Attributes

Original Data Source: Financial News Headlines Data

The main stock market index of United States, the US500, fell to 6260 points on July 11, 2025, losing 0.33% from the previous session. Over the past month, the index has climbed 3.55% and is up 11.48% 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 July of 2025.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates (EMVMACROINTEREST) from Jan 1985 to Jun 2025 about volatility, uncertainty, equity, interest rate, interest, rate, and USA.

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.

The Dow Jones U.S. Completion Total Stock Market Index

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

Market Overview

The global Predictive AI in Stock Market sector is projected to witness robust growth in the coming years. The market size is anticipated to reach approximately USD 4,100.6 million by 2034, rising from an estimated USD 831.5 million in 2024. This expansion reflects a strong compound annual growth rate (CAGR) of 17.3% during the forecast period spanning 2025 to 2034.

This growth can be attributed to the increasing reliance on artificial intelligence to enhance trading strategies, forecast market movements, and support data-driven investment decisions. As financial institutions and individual investors continue to seek better accuracy in forecasting and risk management, the adoption of predictive AI tools is expected to accelerate.

In 2024, North America emerged as the leading regional market, accounting for more than 34.1% of the global revenue share. This equated to a market value of USD 283.5 million. The region’s dominance is driven by early technology adoption, well-established financial infrastructure, and the presence of key AI solution providers.

The US stock market declined as Nvidia shares dropped, affecting major indices. Investors are cautious ahead of the Federal Reserve's policy meeting.

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 July 6 of 2025.

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.

The cover image was taken from https://hipwallpaper.com/stock-trader-wallpapers/

Original Data Source: Financial News Headlines Data

According to Cognitive Market Research, the global stock market size will be USD 3645.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 13% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1458.1 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1093.6 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 838.4 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 182.3 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 72.9 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.
The broker end users held the highest stock market revenue share in 2024.

Market Dynamics of Stock Market

Key Drivers for the Stock Market

Rising Demand for Real-Time Data and Analytics to be an Emerging Market Trend

The increasing need for real-time data and advanced analytics is a significant driver in the stock trading and investing market growth. Investors and traders require up-to-the-minute information on stock prices, market trends, and financial news to make informed decisions quickly. As financial markets become more dynamic and competitive, the ability to access and analyze real-time data becomes crucial for success. Trading applications that offer real-time updates, advanced charting tools, and detailed analytics provide users with a competitive edge by enabling them to react swiftly to market movements. This heightened demand for real-time insights fuels the development and adoption of sophisticated trading platforms that cater to both professional traders and retail investors seeking to maximize their investment opportunities.

Increasing Adoption of Mobile Trading Platforms to Boost Market Growth

The rapid adoption of mobile trading platforms is another key driver for the stock market expansion. With the proliferation of smartphones and mobile internet access, investors are increasingly favoring mobile platforms for their trading activities due to their convenience and accessibility. Mobile trading apps offer users the ability to trade, monitor portfolios, and access financial information on the go, which appeals to both active traders and casual investors. This shift towards mobile platforms is supported by innovations in-app functionality, user experience, and security features. As more investors seek flexibility and real-time engagement with their investments, the demand for sophisticated and user-friendly mobile trading applications continues to rise, propelling market growth.

Restraint Factor for the Stock Market

Stringent Rules and Regulations to Impede the Adoption of Online Trading Platforms

Regulatory compliance and legal challenges are major restraints for the stock trading and investing market share. The financial industry is heavily regulated, with strict rules governing trading practices, data protection, and financial disclosures. Compliance with these regulations requires substantial investment in legal expertise, technology, and administrative processes. Changes in regulations can also introduce uncertainty and additional compliance costs for application providers. For example, regulations such as the Markets in Financial Instruments Directive II (MiFID II) in Europe and the Dodd-Frank Act in the U.S. impose stringent requirements on trading practices and transparency. Failure to adhere to these regulations can result in legal penalties and damage to a company’s reputation, which can inhibit market growth and innovation in trading applications.

Market Volatility and Investor Uncertainty

The stock market is highly sensitive to global economic conditions, geopolitical tensions, interest rate fluctuations, and unexpected events (such as pandemics or wars). This inherent volatility can lead to sharp declines in investor confidence and capital outflows, especially among retai...

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Business Investment And Sentiment (EMVMACROBUS) from Jan 1985 to May 2025 about volatility, uncertainty, equity, investment, business, and USA.

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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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.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Consumer Spending And Sentiment (EMVMACROCONSUME) from Jan 1985 to May 2025 about volatility, uncertainty, equity, PCE, consumption expenditures, consumption, personal, 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.

Introduction

The Global Stock Trading and Investing Applications Market is experiencing significant growth, projected to reach USD 150 billion by 2032, up from USD 39.8 billion in 2023. This growth represents a strong CAGR of 16.4% from 2023 to 2032. The increasing popularity of self-directed investing, advancements in mobile trading technologies, and the rise of robo-advisors are key factors driving the market. As more investors adopt digital platforms for trading and investment, the market is witnessing a shift towards more accessible, efficient, and user-friendly solutions in stock trading, making investing easier for both retail and institutional investors.

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.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Broad Quantity Indicators (EMVMACROBROAD) from Jan 1985 to Jun 2025 about volatility, uncertainty, equity, broad, indexes, and USA.

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… See the full description on the dataset page: https://huggingface.co/datasets/Zihan1004/FNSPID.

US stock futures rose with anticipation building around Nvidia's earnings report, amidst ongoing concerns about tariffs and export controls in the chip industry.