As of December 2024, the Shanghai Stock Exchange had the largest domestic market capitalization among stock exchanges in the Asia Pacific region, amounting to approximately *** trillion U.S. dollars. Second in the ranking was the Shanghai Stock Exchange Group, followed by the Shenzhen Stock Exchange. Stock exchanges in Asia PacificThe major stock exchanges in the Asia-Pacific region are the Tokyo Stock Exchange in Japan, the Shanghai and Shenzhen Stock Exchange in Mainland China, the Hong Kong Stock Exchange in Hong Kong, and the Bombay Stock Exchange in India, which is also the oldest stock exchange in Asia. Also, five out of the ten largest stock exchange operators in the world are located in Asia.What is market capitalization?Market capitalization, also commonly referred to as market cap, is a measure of the total market value of outstanding shares of a company on the stock market. It indicates a company’s relative size and value while taking various determinants such as risk and the market’s perception into consideration. There are large-cap (>** billion), mid-cap (* to ** billion) and small-cap (*** million to * billion) companies depending on their market capitalization.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
China's main stock market index, the SHANGHAI, rose to 3448 points on July 1, 2025, gaining 0.11% from the previous session. Over the past month, the index has climbed 2.57% and is up 15.06% 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 value of global domestic equity market increased from 65.04 trillion U.S. dollars in 2013 to 124.63 trillion U.S. dollars in 2023. 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 62 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 60 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 market cap of 177 billion U.S. dollars as of March 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Bank Central Asia stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.
https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This study examines the market return spillovers from the US market to 10 Asia-Pacific stock markets, accounting for approximately 91 per cent of the region’s GDP from 1991 to 2022. Our findings indicate an increased return spillover from the US stock market to the Asia-Pacific stock market over time, particularly after major global events such as the 1997 Asian and the 2008 global financial crises, the 2015 China stock market crash, and the COVID-19 pandemic. The 2008 global financial crisis had the most substantial impact on these events. In addition, the findings also indicate that US economic policy uncertainty and US geopolitical risk significantly affect spillovers from the US to the Asia-Pacific markets. In contrast, the geopolitical risk of Asia-Pacific countries reduces these spillovers. The study also highlights the significant impact of information and communication technologies (ICT) on these spillovers. Given the increasing integration of global financial markets, the findings of this research are expected to provide valuable policy implications for investors and policymakers.
https://www.imrmarketreports.com/privacy-policy/https://www.imrmarketreports.com/privacy-policy/
The Asia Pacific Current Transducer report features an extensive regional analysis, identifying market penetration levels across major geographic areas. It highlights regional growth trends and opportunities, allowing businesses to tailor their market entry strategies and maximize growth in specific regions.
https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Thailand SET: Turnover: Value: Buy: Asia Plus Securities data was reported at 15,017.525 THB mn in Nov 2018. This records a decrease from the previous number of 23,390.248 THB mn for Oct 2018. Thailand SET: Turnover: Value: Buy: Asia Plus Securities data is updated monthly, averaging 22,888.481 THB mn from Jan 2004 (Median) to Nov 2018, with 179 observations. The data reached an all-time high of 139,281.823 THB mn in Jul 2017 and a record low of 2,538.553 THB mn in Mar 2013. Thailand SET: Turnover: Value: Buy: Asia Plus Securities data remains active status in CEIC and is reported by The Stock Exchange of Thailand. The data is categorized under Global Database’s Thailand – Table TH.Z013: The Stock Exchange of Thailand: Turnover Value by Broker: SET.
https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Examining stock market interactions between China (mainland China and Hong Kong), Japan, and South Korea, this study employs a framework that includes 239 economic variables to identify the spillover effects among these three countries, and empirically simulates the dynamic time-varying non-linear relationship between the stock markets of different countries. The findings are that in recent decades, China's stock market relied on Hong Kong's as a window to the exchange of price information with Japan and South Korea. More recently, the China stock market's spillover effect on East Asia has expanded. The spread of the crisis has strengthened co-movement between the stock markets of China, Japan, and South Korea.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Examining stock market interactions between China (mainland China and Hong Kong), Japan, and South Korea, this study employs a framework that includes 239 economic variables to identify the spillover effects among these three countries, and empirically simulates the dynamic time-varying non-linear relationship between the stock markets of different countries. The findings are that in recent decades, China's stock market relied on Hong Kong's as a window to the exchange of price information with Japan and South Korea. More recently, the China stock market's spillover effect on East Asia has expanded. The spread of the crisis has strengthened co-movement between the stock markets of China, Japan, and South Korea.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
To investigate the issue of inflation-hedging to find appropriate hedging assets against inflation by using the VAR or VECM model. We have collected data encompassing housing price indices, stock indices, price indexes, and money supply from five countries: the United States, Hong Kong, South Korea, Singapore, and Taiwan. The housing price index focuses on the transaction prices of listed residential houses in the metropolitan area as the benchmark, the stock price index is the ordinary stock market index of various countries, the price index is the consumer price index (CPI), and the money supply is M2 aggregate. The time period for obtaining data on the housing price index and stock price index is not the same.
https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy
The Asia Pacific Metal Cans Market size was valued at USD 14.23 billion in 2023 and is projected to reach USD 22.26 billion by 2032, exhibiting a CAGR of 6.6 % during the forecasts period. The Asia Pacific Metal Cans Market concerns the business in the Asia Pacific region that manufactures and markets the metal cans mainly from aluminium or tinplate material. Populated primarily as food and non-food packaging, these cans are used to package food and beverages such as carbonated drinks, beer, canned foods, and dairy products owing to their metallic body’s durability and recyclability, and its capacity to help sustain a product’s freshness. These includes the food industry, the medicament manufacturing industry, and the producers of household chemicals. Current trends and opportunities are: The desire for unusually eco-friendly packaging materials some of the most innovative trends within the packaging market of canned goods – changes in the design of cans for more attractive appearance and convenience; and finally, the use of advanced technologies in the production process. The factors encouraging population penetration in the Asia Pacific region are increasing disposable income, development of cities and towns, and the change of consumer habits where there is a preference for convenient and biodegradable packages.
https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy
Recent developments include: July 2022: The Indian government announced that it had set a target to raise the share of natural gas in the energy mix to 15% by 2030 from the current 6.3%. According to the data demonstrated by the Ministry of Petroleum & Natural Gas, 95.21 lakh PNG (Domestic) connections have been provided, and the authorized entities have established 4531 CNG (Transport) stations as of 31 May 2022., May 2022: The China National Offshore Oil Company (CNOOC) awarded CNY 16 billion (USD2.42 billion) contracts for building 12 liquefied natural gas tankers. The 12 vessels will be constructed by Hudong Zhonghua Shipbuilding Co., a China State Shipbuilding Corporation (CSSC). Each tanker can carry about 174,000 cubic meters of LNG, equivalent to 108 million cubic meters when re-gasified. The vessels are slated for commissioning between 2024 and 2027., January 2022: GAIL (India) Ltd commenced India's first-of-its-kind project of mixing hydrogen into the natural gas system in Indore, Madhya Pradesh. The hydrogen blended natural gas will be supplied to Avantika Gas Ltd, one of GAIL's joint ventures with HPCL, to retail CNG to automobiles and piped natural gas to households in Indore.. Key drivers for this market are: 4., Increasing Electricity Demand4.; Rsing Investments in the Coal Industry. Potential restraints include: 4., Increasing Installation of Renewable Energy Sources. Notable trends are: Increasing Investments in Natural Gas Production to Drive the Market.
https://www.imrmarketreports.com/privacy-policy/https://www.imrmarketreports.com/privacy-policy/
The Asia Pacific Oral Care market report offers a thorough competitive analysis, mapping key players’ strategies, market share, and business models. It provides insights into competitor dynamics, helping companies align their strategies with the current market landscape and future trends.
https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Although the seventh eigenmode is outside the RRS range, we exclude this mode as insignificant as it is very close to the boundary.
This statistic shows the market share of the Carlsberg Group in Asia in 2023, by country. That year, the market share of the company was highest in Laos, with ** percent. In Nepal, the company reached a share of ** percent. The Carlsberg Group had a market share of ***** percent in Vietnam.
https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy
The Asia Pacific Digital Pathology Market size was valued at USD 227.3 million in 2023 and is projected to reach USD 434.56 million by 2032, exhibiting a CAGR of 9.7 % during the forecasts period. Digital pathology is a dynamic, image-based environment that enables the acquisition, management, and interpretation of pathology information generated from digitized glass slides. This innovative field leverages computer technology to convert traditional glass slides into digital slides that can be easily viewed, shared, and analyzed on a computer monitor. With the advent of whole-slide imaging (WSI), digital pathology has become increasingly prevalent, offering a new paradigm for diagnostics and research. The process begins with the scanning of glass slides to create high-resolution digital images. These images can then be stored in a database and accessed remotely, facilitating telepathology and collaborative studies. Digital pathology supports automated image analysis, which enhances the accuracy and efficiency of diagnosing diseases. Recent developments include: In June 2023, Aignostics announced the launch of a collaboration with Virchow Laboratories to advance the use of AI-powered pathology in China in both research and clinical routine. Aignostics is planning to deploy its platform in China at Virchow Laboratories’ sites and enable local AI-powered testing of samples per Chinese regulations , In October 2023, Intralink undertook a business expansion program for Diagnexia across Singapore, Malaysia, Thailand, Indonesia, and China. The company is strategically planning its global expansion drive with a focus on Southeast Asia and China .
As of December 2024, the Shanghai Stock Exchange had the largest domestic market capitalization among stock exchanges in the Asia Pacific region, amounting to approximately *** trillion U.S. dollars. Second in the ranking was the Shanghai Stock Exchange Group, followed by the Shenzhen Stock Exchange. Stock exchanges in Asia PacificThe major stock exchanges in the Asia-Pacific region are the Tokyo Stock Exchange in Japan, the Shanghai and Shenzhen Stock Exchange in Mainland China, the Hong Kong Stock Exchange in Hong Kong, and the Bombay Stock Exchange in India, which is also the oldest stock exchange in Asia. Also, five out of the ten largest stock exchange operators in the world are located in Asia.What is market capitalization?Market capitalization, also commonly referred to as market cap, is a measure of the total market value of outstanding shares of a company on the stock market. It indicates a company’s relative size and value while taking various determinants such as risk and the market’s perception into consideration. There are large-cap (>** billion), mid-cap (* to ** billion) and small-cap (*** million to * billion) companies depending on their market capitalization.