The main stock market index of United States, the US500, fell to 6369 points on March 27, 2026, losing 1.67% from the previous session. Over the past month, the index has declined 7.45%, though it remains 14.12% 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 March of 2026.

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.

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 March 28 of 2026.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

The Dow Jones Industrial Average is (DJIA) is possibly the most well-known and commonly used stock index in the United States. It is a price-weighted index that assesses the stock prices of 30 prominent companies, whose combined prices are then divided by a regularly-updated divisor (0.15199 in February 2021), which gives the index value. The companies included are rotated in and out on a regular basis; as of mid-2022, the longest mainstay on the list is Procter & Gamble, which was added in 1932; whereas Amgen, Salesforce, and Honeywell were all added in 2020. As one of the oldest indices for stock market analysis, the impact of major events, recessions, and economic shocks or booms can be tracked and contextualized over longer periods of time.

Due to inflation, unadjusted figures appear to be more sporadic in recent years, however the greatest fluctuations came in the earliest years of the index. In the given period, the greatest decline came in the wake of the Wall Street Crash in 1929; by 1932 average values had fallen to just one fifth of their 1929 average, from roughly 314 to 65.

The Volatility Index calculated by the Chicago Board Options Exchange. It is a benchmark of financial volatility computed from options on the S&P 500 Index (SPX). It's intention is to measure the expected future volatility on the S&P in the following 30 days.

Sourced from historical data available here https://www.cboe.com/tradable_products/vix/vix_historical_data/

Japan's main stock market index, the JP225, fell to 52005 points on March 27, 2026, losing 2.98% from the previous session. Over the past month, the index has declined 10.42%, though it remains 40.10% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on March of 2026.

In 2021, the Nasdaq 100 closed at ********* points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. The index value closed at ********* points in 2024, an increase of more than ***** points compared to its closing value for the previous year. What does the NASDAQ tell us? The Nasdaq 100 index is comprised of 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. Financial firms are represented by the NASDAQ Bank Index. A stock market index is a measurement of average performance of companies forming the index. It gives a snapshot of what investors are thinking at that particular moment. Other indices The Dow Jones Industrial Average gets more attention than the NASDAQ 100, though it only represents ** companies. It’s best and worst days mark some of the major financial events of the past century. This helps to put more meaning behind events like Black Monday, the Wall Street crash of 1929, or the 2008 Financial Crisis, as well as the speed of their recoveries in financial markets.

The statistic shows the worst days of the Dow Jones Industrial Average index from 1897 to 2025. The worst day in the history of the index was ****************, when the index value decreased by ***** percent. The largest single day loss in points was on ***********.

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...

CBOE Volatility Index (VIX) time-series dataset including daily open, close, high and low. The CBOE Volatility Index (VIX) is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices introduced in 1993.

In 1993, the Chicago Board Options Exchange® (CBOE®) introduced the CBOE Volatility Index®, VIX®, and it quickly became the benchmark for stock market volatility. It is widely followed and has been cited in hundreds of news articles in the Wall Street Journal, Barron’s and other leading financial publications. Since volatility often signifies financial turmoil, VIX is often referred to as the “investor fear gauge”.

VIX measures market expectation of near term volatility conveyed by stock index option prices. The original VIX was constructed using the implied volatilities of eight different OEX option series so that, at any given time, it represented the implied volatility of a hypothetical at-the-money OEX option with exactly 30 days to expiration.

The New VIX still measures the market’s expectation of 30-day volatility, but in a way that conforms to the latest thinking and research among industry practitioners. The New VIX is based on S&P 500 index option prices and incorporates information from the volatility “skew” by using a wider range of strike prices rather than just at-the-money series.

South Korea's main stock market index, the KOSPI, fell to 5439 points on March 27, 2026, losing 0.40% from the previous session. Over the past month, the index has declined 6.10%, though it remains 112.62% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from South Korea. South Korea Stock Market - values, historical data, forecasts and news - updated on March of 2026.

View weekly updates and historical trends for US Investor Sentiment, % Bull-Bear Spread. from United States. Source: The American Association of Individua…

Historical Volatility data for DAX (GDAXI), with performance analysis, and comparisons

Historical Volatility data for CAC 40 (FCHI), with performance analysis, and comparisons

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?

Over the course of their first terms in office, no U.S. president in the past 100 years saw as much of a decline in stock prices as Herbert Hoover, and none saw as much of an increase as Franklin D. Roosevelt (FDR) - these were the two presidents in office during the Great Depression. While Hoover is not generally considered to have caused the Wall Street Crash in 1929, less than a year into his term in office, he is viewed as having contributed to its fall, and exacerbating the economic collapse that followed. In contrast, Roosevelt is viewed as overseeing the economic recovery and restoring faith in the stock market played an important role in this.

By the end of Hoover's time in office, stock prices were 82 percent lower than when he entered the White House, whereas prices had risen by 237 percent by the end of Roosevelt's first term. While this is the largest price gain of any president within just one term, it is important to note that stock prices were valued at 317 on the Dow Jones index when Hoover took office, but just 51 when FDR took office four years later - stock prices had peaked in August 1929 at 380 on the Dow Jones index, but the highest they ever reached under FDR was 187, and it was not until late 1954 that they reached pre-Crash levels once more.

Kitchener street index wall size map.PDF document updates daily. The date above is the date the document link was created or last updated. Check the date in the pdf for the date it was created.

The BEL20, the index of the ** biggest stocks on Euronext Brussels, saw a loss roughly ** percent within four weeks in early 2020 due to economic uncertainties following the coronavirus pandemic. On *******************, the index reached its highest point in over 13 years. One month after, however, this had all but evaporated. In the middle of March, a correction followed in the wake of a bear rally in Wall Street and rumors of the use of helicopter money (tax-free money handed out by the government to consumers). As of ************* the BEL20 index stood at ******** point.

Kitchener street index with wards wall size map.PDF document updates daily. The date above is the date the document link was created or last updated. Check the date in the pdf for the date it was created.