The main stock market index of United States, the US500, fell to 6742 points on April 12, 2026, losing 1.09% from the previous session. Over the past month, the index has climbed 1.66% and is up 24.72% 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.

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

Stock Market Data 2025

This synthetic dataset contains comprehensive daily trading information for 81 major S&P 500 companies spanning in 2025. The data represents synthetically generated but highly realistic stock market conditions with accurate price ranges, sector distributions, and financial metrics that mirror real-world market behavior.

Dataset Overview

Temporal Coverage: 53 trading days (August 1 - August 31, 2025)
Market Universe: 81 S&P 500 constituent companies
Total Records: ~4,293 daily stock entries
Market Context: S&P 500 level at 6,310 with total market cap of $52.5T

Data Schema & Structure

Column Name	Data Type	Description	Example Range
Date	String (YYYY-MM-DD)	Trading date	2025-08-01 to 2025-08-31
Ticker	String	Stock ticker symbol	AAPL, MSFT, NVDA, etc.
Open Price	Float	Opening price for trading day (USD)	$19.00 - $3,800.00
Close Price	Float	Closing price for trading day (USD)	$19.00 - $3,850.00
High Price	Float	Intraday highest price (USD)	$19.50 - $3,900.00
Low Price	Float	Intraday lowest price (USD)	$18.50 - $3,750.00
Volume Traded	Integer	Number of shares traded	500K - 90M shares
Market Cap	Float	Market capitalization (USD)	$68B - $3.2T
PE Ratio	Float	Price-to-Earnings ratio	8.0 - 85.0
Dividend Yield	Float	Annual dividend yield (%)	0.0% - 7.1%
EPS	Float	Earnings Per Share (USD)	$1.50 - $70.00
52 Week High	Float	Highest price in past 52 weeks (USD)	$25.00 - $4,000.00
52 Week Low	Float	Lowest price in past 52 weeks (USD)	$15.00 - $3,200.00
Sector	String	Industry sector classification	10 GICS sectors

Market Composition & Sector Distribution

Sector Breakdown

Sector	Companies	Percentage	Avg Market Cap
Technology	18	22.2%	$850B
Healthcare	15	18.5%	$280B
Financials	14	17.3%	$290B
Consumer Discretionary	8	9.9%	$320B
Consumer Staples	8	9.9%	$310B
Communication Services	5	6.2%	$480B
Industrials	7	8.6%	$155B
Energy	2	2.5%	$385B
Utilities	3	3.7%	$110B
Real Estate	2	2.5%	$110B

Market Capitalization Tiers

• Mega Cap (>$1T): 6 companies (AAPL, MSFT, NVDA, AMZN, GOOGL, META)
• Large Cap ($200B-$1T): 28 companies
• Mid Cap ($50B-$200B): 47 companies

Key Market Characteristics

Price Volatility by Sector

• Technology: Higher volatility (±3.5% daily range)
• Energy: High volatility (±4.0% daily range)
• Utilities: Lower volatility (±1.5% daily range)
• Healthcare/Financials: Moderate volatility (±2.5% daily range)

Trading Volume Patterns

• Mega Cap: 25M - 90M shares daily
• Large Cap: 8M - 35M shares daily
• Mid Cap: 2M - 15M shares daily
• Small Cap: 500K - 5M shares daily

Financial Metrics Distribution

• Average P/E Ratio: 25.9 (market-wide)
• Average Dividend Yield: 1.25%
• Price Range: $19 (T) to $3,850 (BKNG)
• EPS Range: $1.50 to $70.00

Notable Market Features

High-Value Stocks

• BKNG (Booking Holdings): $3,650-$3,850 range
• AVGO (Broadcom): $1,650-$1,750 range
• REGN (Regeneron): $1,050-$1,150 range
• LLY (Eli Lilly): $920-$980 range

High-Dividend Yielders

• T (AT&T): 7.1% dividend yield
• VZ (Verizon): 6.2% dividend yield
• PFE (Pfizer): 5.8% dividend yield

Growth & Technology Leaders

• NOW (ServiceNow): P/E ratio of 85
• NVDA (NVIDIA): P/E ratio of 45
• TSLA (Tesla): P/E ratio of 55

Data Quality & Realism Features

✅ Authentic Price Ranges: Based on realistic 2025 market projections
✅ Sector-Appropriate Volatility: Different volatility patterns by industry
✅ Correlated Metrics: P/E ratios, dividend yields, and EPS align with market caps
✅ Realistic Trading Volumes: Volume scaled appropriately to market cap
✅ Temporal Consistency: Logical price progression over 53-day period
✅ Market Cap Accuracy: Daily fluctuations reflect actual price movements

Intended Use Cases

• Financial Analysis & Modeling: Portfolio optimization, risk assessment
• Machine Learning Applications: Predictive modeling, algorithmic trading
• Educational Purposes: Finance courses, data science training
• Algorithm Development: Backtesting trading strategies
• Market Research: Sector analysis, correlation studies
• Visualization Projects: Interactive dashboards, market trend analysis

This dataset provides a comprehensive foundation for quantitative finance research, offering both breadth across market sectors and depth in daily trading dynamics while maintaining statisti...

This dataset integrates multiple financial data sources to enable detailed stock market trend analysis and decision-making.

Key Features:

Daily Stock Trading Metrics – Includes open, high, low, close prices, and trading volume.

Macroeconomic Indicators – Covers GDP growth, inflation rates, and interest rates.

Sentiment-Labeled News – Financial news articles with positive, negative, or neutral sentiment tags.

Multisource Integration – Combines structured and unstructured financial data for deeper insights.

Comprehensive Market Coverage – Designed for stock trend analysis, investment strategies, and risk assessment.

Supports Predictive Modeling – Enables better understanding of market dynamics and investor sentiment.

Dataset Overview

This dataset contains comprehensive stock market data for June 2025, capturing daily trading information across multiple companies and sectors. The dataset represents a substantial collection of market data with detailed financial metrics and trading statistics.

Basic Dataset Information

• Time Period: June 1-21, 2025 (21 trading days)
• Total Records: Approximately 11,600+ entries
• Companies Covered: 500+ unique stocks
• Data Type: Daily stock market trading data with fundamental metrics

Markdown Table Format

Column Name	Data Type	Description	Example Values
Date	Date	Trading date in DD-MM-YYYY format	01-06-2025, 02-06-2025
Ticker	String	Stock ticker symbol (3-4 characters)	AAPL, GOOGL, TSLA
Open Price	Float	Opening price of the stock	34.92, 206.5, 125.1

Dataset Information Table

Dataset Overview

Attribute	Details
Dataset Name	Stock Market Data - June 2025
File Format	CSV
File Size	~2.5 MB
Number of Records	11,600+
Number of Features	13
Time Period	June 1-21, 2025

Data Schema

Column Name	Data Type	Description	Example Values
Date	Date	Trading date in DD-MM-YYYY format	01-06-2025, 02-06-2025
Ticker	String	Stock ticker symbol (3-4 characters)	AAPL, GOOGL, TSLA, SLH
Open Price	Float	Opening price of the stock	34.92, 206.5, 125.1
Close Price	Float	Closing price of the stock	34.53, 208.45, 124.03
High Price	Float	Highest price during the trading day	35.22, 210.51, 127.4
Low Price	Float	Lowest price during the trading day	34.38, 205.12, 121.77
Volume Traded	Integer	Number of shares traded	2,966,611, 1,658,738
Market Cap	Float	Market capitalization in dollars	57,381,363,838.88
PE Ratio	Float	Price-to-Earnings ratio	29.63, 13.03, 29.19
Dividend Yield	Float	Dividend yield percentage	2.85, 2.73, 2.64
EPS	Float	Earnings per Share	1.17, 16.0, 4.25
52 Week High	Float	Highest price in the last 52 weeks	39.39, 227.38, 138.35
52 Week Low	Float	Lowest price in the last 52 weeks	28.44, 136.79, 100.69
Sector	String	Industry sector classification	Industrials, Energy, Healthcare

Market Capitalization Tiers

• Mega Cap (>$1T): 6 companies (AAPL, MSFT, NVDA, AMZN, GOOGL, META)
• Large Cap ($200B-$1T): 28 companies
• Mid Cap ($50B-$200B): 47 companies

Key Market Characteristics

Price Volatility by Sector

• Technology: Higher volatility (±3.5% daily range)
• Energy: High volatility (±4.0% daily range)
• Utilities: Lower volatility (±1.5% daily range)
• Healthcare/Financials: Moderate volatility (±2.5% daily range)

Trading Volume Patterns

• Mega Cap: 25M - 90M shares daily
• Large Cap: 8M - 35M shares daily
• Mid Cap: 2M - 15M shares daily
• Small Cap: 500K - 5M shares daily

Financial Metrics Distribution

• Average P/E Ratio: 25.9 (market-wide)
• Average Dividend Yield: 1.25%
• Price Range: $19 (T) to $3,850 (BKNG)
• EPS Range: $1.50 to $70.00

Notable Market Features

High-Value Stocks

• BKNG (Booking Holdings): $3,650-$3,850 range
• AVGO (Broadcom): $1,650-$1,750 range
• REGN (Regeneron): $1,050-$1,150 range
• LLY (Eli Lilly): $920-$980 range

High-Dividend Yielders

• T (AT&T): 7.1% dividend yield
• VZ (Verizon): 6.2% dividend yield
• PFE (Pfizer): 5.8% dividend yield

Growth & Technology Leaders

• NOW (ServiceNow): P/E ratio of 85
• NVDA (NVIDIA): P/E ratio of 45
• TSLA (Tesla): P/E ratio of 55

Data Quality & Realism Features

✅ Authentic Price Ranges: Based on realistic 2025 market projections ✅ Sector-Appropriate Volatility: Different volatility patterns by industry ✅ Correlated Metrics: P/E ratios, dividend yields, and EPS align with market caps ✅ Realistic Trading Volumes: Volume scaled appropriately to market cap ✅ Temporal Consistency: Logical price progression over 53-day period ✅ Market Cap Accuracy: Daily fluctuations reflect actual price movements

Intended Use Cases

• Financial Analysis & Modeling: Portfolio optimization, risk assessment
• Machine Learning Applications: Predictive modeling, algorithmic trading
• Educational Purposes: Finance courses, data science training
• Algorithm Development: Backtesting trading strategies
• Market Research: Sector analysis, correlation studies
• Visualization Projects: Interactive dashboards, market trend analysis

This dataset provides a comprehensive foundation for quantitative finance research, offering both breadth across market sectors and depth in daily trading dynamics while maintaining statistical realism throughout the observation period...

The main stock market index of United States, the US500, rose to 6899 points on February 25, 2026, gaining 0.13% from the previous session. Over the past month, the index has declined 0.73%, though it remains 15.84% higher than a year ago, 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 February of 2026.

United Kingdom's main stock market index, the GB100, fell to 10583 points on April 13, 2026, losing 0.17% from the previous session. Over the past month, the index has climbed 2.57% and is up 30.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on April of 2026.

The main stock market index of United States, the US500, fell to 6773 points on April 13, 2026, losing 0.64% from the previous session. Over the past month, the index has climbed 1.10% and is up 25.29% 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.

The stock price data represents the daily closing prices of multiple stocks over a specified time period. Each stock's price fluctuates based on various factors such as market demand, company performance, economic conditions, and investor sentiment. The dataset provides a snapshot of stock price movements, allowing analysts to observe trends, patterns, and potential relationships between different stocks over time.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Graph and download economic data for Index of Preferred Stock Prices, New York Stock Exchange for United States (M11008USM322NNBR) from Jan 1902 to May 1923 about stock market, New York, indexes, and USA.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2016-04-11 to 2026-04-10 about stock market, average, industry, and USA.

The value of the DJIA index amounted to ********* at the end of February 2026, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

Hong Kong's main stock market index, the HK50, fell to 25589 points on April 13, 2026, losing 1.17% from the previous session. Over the past month, the index has declined 0.95%, though it remains 19.48% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on April of 2026.

Germany's main stock market index, the DE40, fell to 23534 points on April 13, 2026, losing 1.14% from the previous session. Over the past month, the index has declined 0.13%, though it remains 12.31% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on April of 2026.

Stock market index in the USA, February, 2026 The most recent value is 217.92 points as of February 2026, an increase compared to the previous value of 212.01 points. Historically, the average for the USA from January 1960 to February 2026 is 47.1 points. The minimum of 2.98 points was recorded in June 1962, while the maximum of 217.92 points was reached in February 2026. | TheGlobalEconomy.com

The Rolling Stock Market Report is Segmented by Type (Locomotives, Metros and Light Rail Vehicles, Passenger Coaches, and More), Propulsion Type (Diesel, Electric, and More), Application (Passenger Rail and Freight Rail), End-User (National Rail Operators and More), Technology (Conventional and More) and Geography. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Units).

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

🌍 Overview

This dataset provides daily stock market indicators for major global indices across North America, Europe, and Asia. It is designed for data analysis, machine learning, forecasting, and business intelligence projects.

The dataset captures essential price movements and trading activity, making it ideal for understanding market trends, volatility, and global financial behavior.

📅 Time Period

• Start Date: January 2020
• End Date: December 2024
• Frequency: Daily

🏦 Stock Market Indices Covered

• 🇺🇸 S&P 500
• 🇺🇸 NASDAQ Composite
• 🇺🇸 Dow Jones Industrial Average
• 🇬🇧 FTSE 100
• 🇯🇵 Nikkei 225
• 🇭🇰 Hang Seng
• 🇩🇪 DAX
• 🇫🇷 CAC 40
• 🇨🇳 SSE Composite
• 🇵🇰 KSE 100

🧾 Dataset Columns Explained

Column Name	Description
📅 Date	Trading date
📈 Index_Name	Name of the stock market index
🌍 Country	Country where the index operates
🔓 Open	Opening price of the index
🔺 High	Highest price during the day
🔻 Low	Lowest price during the day
🔒 Close	Closing price of the index
📊 Volume	Total trading volume
📉 Daily_Change_Percent	Percentage change from open to close

snapshot-tab-pane Predictive AI In Stock Market Size 2025-2029The predictive AI in stock market size is valued to increase by USD 1.63 billion, at a CAGR of 21.8% from 2024 to 2029. Increasing availability and integration of alternative data will drive the predictive AI in stock market.Market InsightsNorth America dominated the market and accounted for a 33% growth during the 2025-2029.By Component - Solution segment was valued at USD 329.80 billion in 2023By Application - Algorithmic trading segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 445.64 million Market Future Opportunities 2024: USD 1632.20 millionCAGR from 2024 to 2029 : 21.8%Market SummaryPredictive AI in the stock market refers to the application of artificial intelligence (AI) algorithms and techniques to analyze historical market data and make predictions about future trends. This technology has gained significant attention in recent years due to the increasing availability and integration of alternative data sources and the advancement of generative AI and large language models for qualitative alpha generation. One real-world business scenario where predictive AI is making a significant impact is in supply chain optimization. For instance, a manufacturing company can use predictive AI to forecast demand for its products based on historical sales data, economic indicators, and other external factors.By accurately predicting demand, the company can optimize its inventory levels, reduce carrying costs, and improve operational efficiency. However, the adoption of predictive AI in the stock market also presents several challenges. Data quality and overfitting are major concerns, as historical data may not accurately reflect future market conditions. Market reflexivity, or the phenomenon where market participants' actions influence market trends, can also make it challenging to make accurate predictions. Despite these challenges, the potential benefits of predictive AI in the stock market are significant, including improved risk management, increased operational efficiency, and enhanced investment strategies.What will be the size of the Predictive AI In Stock Market during the forecast period?Get Key Insights on Market Forecast (PDF) Request Free SamplePredictive AI in the stock market is an evolving technology that leverages advanced algorithms and real-time analytics to identify trends and patterns, enabling data-driven decision-making for businesses. One significant trend in this domain is the integration of demand sensing technology, which improves accuracy by reducing false positive and false negative rates. For instance, model performance can be enhanced through algorithm performance improvements, feature engineering techniques, and model retraining frequencies. In the realm of supply chain optimization, predictive AI-powered forecasting plays a pivotal role in inventory control strategies. By monitoring data in real-time, businesses can implement automated ordering systems, ensuring stockout prevention and minimizing excess inventory.This approach not only improves precision and recall but also enables better risk mitigation planning and compliance with data privacy regulations. Scalability testing and data quality management are essential aspects of deploying predictive AI models in the stock market. Hyperparameter tuning and error rate reduction are critical for maintaining model performance, while system monitoring tools facilitate predictive maintenance and performance benchmarks. By adhering to data governance policies, businesses can ensure the reliability and accuracy of their predictive AI models, ultimately leading to improved business intelligence and strategic decision-making.Unpacking the Predictive AI In Stock Market LandscapeThe market management employs advanced clustering techniques and predictive modeling to minimize lead time variability and enhance production planning. By integrating real-time data processing and scalable infrastructure, businesses can achieve significant improvements in inventory optimization and order fulfillment prediction. For instance, predictive models trained on model training datasets have demonstrated a 20% increase in demand prediction accuracy compared to traditional methods. Data security protocols are essential to safeguard sensitive stock market data. Predictive AI systems employ machine learning models, deep learning algorithms, and neural network architecture for model evaluation and classification. These advanced techniques enable real-time anomaly detection and statistical process control, ensuring risk assessment metrics align with business objectives. Cloud-based infrastructure and process automation tools facilitate seamless data integration pipe