The main stock market index of United States, the US500, rose to 6327 points on July 23, 2025, gaining 0.27% from the previous session. Over the past month, the index has climbed 3.85% and is up 16.57% 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.

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 Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

Stocks traded, total value (current US$) in United States was reported at 42600207070000 USD in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Stocks traded, total value - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The value of global domestic equity market increased from ***** trillion U.S. dollars in 2013 to ****** trillion U.S. dollars in 2024. The United States was by far the leading country with the largest share of total world stocks as of 2024. Global market capitalization in different regions The market capitalization of domestic companies listed varied across different regions of the world. As of Decmber 2024, the Americas region had the largest domestic equity market, totaling ** trillion U.S. dollars. This region is home to the NYSE and Nasdaq, which are the two largest stock exchange operators in the world. The market capitalization of these two exchanges alone exceeded ** billion U.S. dollars as of January 2025, larger than the total market capitalization in the Asia-Pacific, and in the EMEA regions in the same period. Largest Stock Exchanges in Latin America As of December 2024, the B3 (Brasil Bolsa Balcao) was the biggest stock exchange in Latin America in terms of market capitalization and the second-largest in terms of number of listed companies. Following the B3 were the Mexican Stock Exchange and the Santiago Stock Exchange in Chile. The most valuable company in Latin America is listed on the Mexican Stock Exchange: Fomento Económico Mexicano, a multinational beverage and retail company headquartered in Monterrey, had a market cap of *** billion U.S. dollars as of March 2025.

Euro Area's main stock market index, the EU50, rose to 5329 points on July 23, 2025, gaining 0.75% from the previous session. Over the past month, the index has climbed 0.60% and is up 9.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013A) from 1970 to 2024 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

The MSCI World index dropped sharply in the four weeks between February 16 and March 15, 2020, shedding 31.3 percent of its value. This, of course, was due to the economic impact of the global coronavirus (COVID-19) pandemic. It was not until November 2020 that the index recovered to the levels seen in early 2020. On July 20, 2025, it reached over 4,086 index points, the highest value during the observed period. 1,583 companies from 23 developed economies are included in the MSCI World Index. While a world index in the sense of covering developed markets in North America, Western Europe, and the Asia-Pacific region, it has been criticized for how it excludes companies located in large developing economies such as China, Russia, Brazil, India, and South Africa.

China's main stock market index, the SHANGHAI, rose to 3582 points on July 22, 2025, gaining 0.62% from the previous session. Over the past month, the index has climbed 5.92% and is up 22.86% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

The NYSE U.S. 100 Index tracks the largest U.S. companies traded on the New York Stock Exchange. This statistic shows the leading 20 companies on the NYSE U.S. 100 Index by market capitalization. As of January 28, 2024 the multinational conglomerate company ****************** ranked as the first, with a market capitalization of over *** billion euros. This was followed by ********* and ***************, with market capitalizations amounting to *** billion and *** billion euros respectively. NYSE U.S. 100 Index vs. Nasdaq 100 Index The New York Stock Exchange and the Nasdaq are the largest two stock exchanges in the world, but they differ in the kinds of companies they list. The NYSE is known to list stable and long-lasting firms, commonly referred to as “blue-chip” companies. In contrast, the Nasdaq is renowned for listing the world’s biggest companies, mainly from the tech industry. Similar to the NYSE U.S. 100 Index, the Nasdaq 100 Index tracks the 100 largest non-financial companies listed on the Nasdaq exchange, including both U.S. and non-U.S. companies. The leader of the NYSE U.S. 100 index: Berkshire Hathaway Berkshire Hathaway, the leader of the NYSE U.S. 100 Index, was also among the world's largest companies by revenue in 2023. The company is a multinational conglomerate and holding company with insurance as its core business and interests in other sectors such as railroad, utilities and energy, finance. In fact, Berkshire was the world's biggest insurance company by revenue in 2023. As a holding company, it has significant stakes in some of the world’s largest companies, including Apple, Bank of America and Coca-Cola. With its diverse background in various businesses and industries, Berkshire Hathaway had a total revenue of *** billion U.S. dollars in 2023.

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.

DJ US Healthcare: Poised for Recovery?

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

United States US: Market Capitalization: Listed Domestic Companies: % of GDP data was reported at 165.651 % in 2017. This records an increase from the previous number of 146.862 % for 2016. United States US: Market Capitalization: Listed Domestic Companies: % of GDP data is updated yearly, averaging 102.679 % from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 165.651 % in 2017 and a record low of 39.352 % in 1981. United States US: Market Capitalization: Listed Domestic Companies: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Graph and download economic data for Rest of the World; Foreign Direct Investment in U.S.: Equity; Asset (Market Value), Level (BOGZ1FL263092141A) from 1945 to 2024 about FDI, market value, equity, assets, and USA.

Argentina Index: BCBA: Merval: US Dollars data was reported at 1,795.593 30Jun1986=0.01 in Apr 2025. This records a decrease from the previous number of 2,177.617 30Jun1986=0.01 for Mar 2025. Argentina Index: BCBA: Merval: US Dollars data is updated monthly, averaging 604.550 30Jun1986=0.01 from Dec 1990 (Median) to Apr 2025, with 413 observations. The data reached an all-time high of 2,453.251 30Jun1986=0.01 in Dec 2024 and a record low of 88.208 30Jun1986=0.01 in May 2002. Argentina Index: BCBA: Merval: US Dollars data remains active status in CEIC and is reported by Buenos Aires Stock Exchange. The data is categorized under Global Database’s Argentina – Table AR.Z001: Buenos Aires Stock Exchange: Index.

In 2024, the yield on 10-year U.S. treasury securities increased to 4.21 percent, up from 3.96 percent in the previous year. 2020 recorded the lowest value in the period under consideration, and well below the longer-term average. In 1980 the yield was 11.43 percent. What are treasury securities? The United States government consistently has a budget deficit, and it finances this spending with debt issued by the Treasury Department. These treasury securities are attractive investments because most investors believe that the United States Treasury Department will never default. For this reason, many investors of different varieties hold these securities. Country differences The markets consider treasury securities to be low-risk, as they are secured by governments. Different countries differ in level of indebtment, value of investments, stability of currency, GDP growth, inflation, etc. These factors are the reasons why yields on government bonds differ from country to country. The yield shows how much a given government has to pay to the investors for the money that it borrows.

The stock market today saw mixed results with major U.S. stock indexes closing higher, while crude oil prices experienced a decline. Factors including a tech sell-off, economic recovery concerns, stimulus package delays, demand outlook, OPEC+ production increase, and U.S. inventory buildup influenced the market and oil prices.

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.

Dow Jones U.S. Select Telecommunications: A Signal of Market Dynamics?

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

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.

Dow Jones U.S. Financials Index Forecast: Mixed Outlook

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

Natural gas fell to 3.25 USD/MMBtu on July 22, 2025, down 2.13% from the previous day. Over the past month, Natural gas's price has fallen 14.59%, but it is still 48.80% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on July of 2025.

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.

Dow Jones U.S. Banks: Banking on a Brighter Future?

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