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.

Sapiens International stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

International Paper stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The International Express Service Market report segments the industry into Shipment Weight (Heavy Weight Shipments, Light Weight Shipments, Medium Weight Shipments), Route (Inter-Region, Intra-Region), End User Industry (E-Commerce, Financial Services (BFSI), Healthcare, Manufacturing, Primary Industry, and more), and Region (Africa, Asia Pacific, Europe, GCC, North America, and more).

The share of total exports from European Union member states which goes to other EU countries underwent a decline from its early 2000s high point during the global financial crisis, great recession and Eurozone crisis (2007-2012), before rebounding back to around 61.77 percent of total exports in 2023. This share is a good indicator of the relative importance of intra-EU trade, that is, trade governed by the "four freedoms" of the European Single Market (freedom of movement for goods, services, capital, and labor), vis-a-vis international trade with partners outside of the European Union. It is worth keeping in mind that the United Kingdom, a key trading partner of many European Union countries, left the EU in 2020, meaning that the country was added to the extra-EU share. The fact that this did not have a notable effect on the share of exports going to extra-EU countries points to the declining relative importance of the UK as a trade partner for the EU.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around ** percent of their value compared to January *, 2020. However, Asian markets and the NASDAQ Composite Index only shed around ** to ** percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around ** percent higher than in January 2020, while most other markets were only between ** and ** percent higher. Why did the NASDAQ recover the quickest? Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide. Which markets suffered the most? The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

In the Midocean University in Comoros, ** percent of students were international students, making it the university in the world with the highest share of foreign students. Some of these universities have a clear focus on remote learning, making it far easier for international students to register and study there. . See here for an overview of the highest ranked universities in the world.

Common-Stock Time Series for International Public Partnership. International Public Partnerships Limited specializes in investments in broader infrastructure sector with a focus on public or social infrastructure. The fund seeks acquisition opportunities that may be single assets, portfolios, shares in companies, or interest in partnerships. It also seeks to invest in private finance initiative and public private partnerships procurement model for public infrastructure in developed countries. The firm invests in opportunities both in United Kingdom and internationally.

Italy Turnover: Italian Stock Exchange: Shares: MTA International data was reported at 123.500 EUR mn in Oct 2018. This records an increase from the previous number of 118.500 EUR mn for Sep 2018. Italy Turnover: Italian Stock Exchange: Shares: MTA International data is updated monthly, averaging 132.400 EUR mn from May 2010 (Median) to Oct 2018, with 102 observations. The data reached an all-time high of 377.500 EUR mn in Feb 2011 and a record low of 59.200 EUR mn in Aug 2014. Italy Turnover: Italian Stock Exchange: Shares: MTA International data remains active status in CEIC and is reported by Italian Stock Exchange. The data is categorized under Global Database’s Italy – Table IT.Z004: Turnover.

During the two decades since 2002, the shares of European Union imports from the rest of the world taken by the individual member states have undergone subtle shifts. In 2021, Germany was still the largest importer, as it was in 2002, however, the Netherlands is the member state which has experienced the greatest increase in its share of imports, increasing from 13 percent to almost 18 percent. France and Italy have experienced declines in their shares of total EU imports, reflecting the economic problems which both have faced since the great recession and Eurozone crisis. Spain and Belgium have remained steady as the fifth and sixth largest importers, while Poland has emerged as a significant import market, doubling its share of EU imports since 2002.

The Tehran Stock Exchange emerged as the global leader in single stock options trading in 2024, with over *** billion contracts traded. This surprising development highlights the growing importance of emerging markets in global financial trading. The Nasdaq - US and B3 - Brasil Bolsa Balcão followed closely behind, demonstrating the continued strength of established exchanges alongside newer entrants.

The GCC International Express Service Market report segments the industry into Shipment Weight (Heavy Weight Shipments, Light Weight Shipments, Medium Weight Shipments), Route (Inter-Region, Intra-Region), End User Industry (E-Commerce, Financial Services (BFSI), Healthcare, Manufacturing, Primary Industry, Wholesale and Retail Trade (Offline), Others), and Country (Qatar, Saudi Arabia, UAE, Rest of GCC).

Hermes International stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Share of tariff lines with international peaks, manufactured products (%) in Japan was reported at 1.927 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Share of tariff lines with international peaks, manufactured products - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Global Share of Scientific Publications Involving International Collaboration in Molecular Biology by Country, 2023 Discover more data with ReportLinker!

National Stock Exchange: Index: BNV Share Index data was reported at 11,895.320 NA in Apr 2025. This records a decrease from the previous number of 11,895.420 NA for Mar 2025. National Stock Exchange: Index: BNV Share Index data is updated monthly, averaging 11,262.265 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 14,829.030 NA in May 2017 and a record low of 5,302.210 NA in Jun 2012. National Stock Exchange: Index: BNV Share Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Costa Rica – Table CR.EDI.SE: National Stock Exchange: Monthly.

Global Share of Scientific Publications Involving International Collaboration in Pharmacy by Country, 2023 Discover more data with ReportLinker!

In January 2025, Google accounted for 93.82 percent of the global mobile search engine market worldwide. Yandex had 2.5 percent of the global mobile search, while, competitors like Baidu and Yahoo! accounted for less than one percent each on a global scale.

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.

AIN Albany International Corporation Common Stock

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.

MAR Marriott International Class A Common Stock

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