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.

In 2025, stock markets in the United States accounted for roughly ** percent of world stocks. The next largest country by stock market share was China, followed by the European Union as a whole. The New York Stock Exchange (NYSE) and the NASDAQ are the largest stock exchange operators worldwide. What is a stock exchange? The first modern publicly traded company was the Dutch East Industry Company, which sold shares to the general public to fund expeditions to Asia. Since then, groups of companies have formed exchanges in which brokers and dealers can come together and make transactions in one space. Stock market indices group companies trading on a given exchange, giving an idea of how they evolve in real time. Appeal of stock ownership Over half of adults in the United States are investing money in the stock market. Stocks are an attractive investment because the possible return is higher than offered by other financial instruments.

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.

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 40 percent of their value compared to January 5, 2020. However, Asian markets and the NASDAQ Composite Index only shed around 20 to 25 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 65 percent higher than in January 2020, while most other markets were only between 20 and 40 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.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

South Korea's main stock market index, the KOSPI, rose to 3072 points on June 30, 2025, gaining 0.52% from the previous session. Over the past month, the index has climbed 13.81% and is up 9.54% compared to the same time last year, 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 June of 2025.

As of April 2025, the Hang Seng Index at the Hong Kong Exchange amounted to ********* points. After the outbreak of COVID-19, the index dropped as part of a broader Pan-Asian trend. However, by the end of 2020, when the pandemic situation stabilized in many countries and news about a vaccine rollout came out, the Hang Seng Index recovered and recorded significant increases every month. Index composition The Hang Seng Index is the most prominent indicator of stock performance on the Hong Kong Exchange. By including the 50 largest companies, the index represents the market movements of more than half of the bourse’s market capitalization. In addition to that, the Hang Seng Index has numerous smaller indices which mirror smaller industries or market sections. The Hang Seng Composite Index One example of a sub-index is the Hang Seng Composite Index. It reflects the performance of the top 95 percentile of the total market capitalization. The financial industry accounted for the largest share of companies included in the index, followed by the information technology sector. Prominent companies represented in the index are Tencent, AIA, and Meituan.

The New York Stock Exchange (NYSE) is the largest stock exchange in the world, with an equity market capitalization of almost ** trillion U.S. dollars as of June 2025. The following three exchanges were the NASDAQ, PINK Exchange, and the Frankfurt Exchange. What is a stock exchange? A stock exchange is a marketplace where stockbrokers, traders, buyers, and sellers can trade in equities products. The largest exchanges have thousands of listed companies. These companies sell shares of their business, giving the general public the opportunity to invest in them. The oldest stock exchange worldwide is the Frankfurt Stock Exchange, founded in the late sixteenth century. Other functions of a stock exchange Since these are publicly traded companies, every firm listed on a stock exchange has had an initial public offering (IPO). The largest IPOs can raise billions of dollars in equity for the firm involved. Related to stock exchanges are derivatives exchanges, where stock options, futures contracts, and other derivatives can be traded.

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.

For this study, India (BSE), Indonesia (JKSE), Shanghai Composite Index (SSE), Taiwan (TWII), Malaysia (KLCI), the Philippines (PSI), Thailand (SET), and South Korea (KOSPI) were selected as the Asia emerging markets. The data source used was DataGuide5 (https://dataguide.fnguide.com), and the sample interval was selected from 2000 to 2024, with closing prices for five trading days per week.

Data consists of 10 Asian countries, Japan, China, Hong Kong, Taiwan, South Korea, Singapore, Malaysia, Thailand, Indonesia, Philippines plus U.S., total 11 countries. Monthly stock market index and nominal exchange rates (end of the month in local currency terms) are collected from Datastream from January 1990 to December 2013 total 288 monthly observations. Each country data in in a separate workbook tab. There are 10 country workbook tab plus US data in the 11th tab. 12th tab workbook describes data for each country. The last tab workbook combines all country data into one single pooled data file.

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.

JPMorgan Asia Growth Income (JAGI) - A Strategic Play on the Rising Asian Tiger

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

Results of the KOSPI200 prediction based on [2] method with 20-day and 30-day moving average.

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.

Voya or ING: Which Asia Pacific High Dividend Equity Income Fund (IAE) Will Reign Supreme?

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

The real-time index database market is experiencing robust growth, driven by the increasing demand for immediate insights from large volumes of streaming data across diverse industries. The market's expansion is fueled by the need for faster data processing and analysis, particularly in applications requiring real-time decision-making, such as fraud detection, cybersecurity threat response, and algorithmic trading. Cloud-based solutions are dominating the market due to their scalability, cost-effectiveness, and ease of deployment, attracting both individual developers and large enterprises. While on-premises deployments still hold a segment of the market, the shift towards cloud is undeniable. Key players like Elastic, Amazon Web Services (AWS), Apache Solr, Splunk, and Microsoft are fiercely competing, constantly innovating to offer enhanced features and performance. The market is geographically diverse, with North America and Europe currently holding significant shares, although rapid growth is anticipated in regions like Asia-Pacific, driven by increasing digitalization and adoption of advanced analytics. The overall market is estimated to be valued at $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 18% between 2025 and 2033, indicating significant future potential. Factors like rising data volumes, increasing need for real-time analytics across diverse sectors, and enhanced data security measures are key drivers, while challenges including data complexity, integration issues, and cost considerations are potential restraints to market expansion. The market segmentation reveals a significant proportion of enterprise users adopting real-time index databases, highlighting the critical role of these technologies in streamlining business operations and improving decision-making capabilities within larger organizations. While individual users contribute to the market, the enterprise segment is a key engine for growth. Future growth will likely be shaped by technological advancements, including the development of more efficient indexing algorithms and enhanced support for diverse data formats. Furthermore, strategic partnerships and mergers & acquisitions will play a crucial role in reshaping the competitive landscape and fostering innovation within the real-time index database market.

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.

AJJ ASIAN AMERICAN MEDICAL GROUP LIMITED

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.

LON:ASIA ASIA STRATEGIC HOLDINGS LIMITED

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

Singapore led the Index of Economic Freedom in 2024, with an index score of 83.5 out of 100. Switzerland, Ireland, Taiwan, and Luxembourg rounded out the top five. Economic Freedom Index In order to calculate the Economic Freedom Index, the source takes 12 different factors into account, including the rule of law, government size, regulatory efficiency, and open markets. All 12 factors are rated on a scale of zero to 100 and are weighted equally. Every country is rated within the Index in order to provide insight into the health and freedom of the global economy. Singapore's economy Singapore is one of the four so-called Asian Tigers, a term used to describe four countries in Asia that saw a booming economic development from the 1950s to the early 1990. Today, the City-State is known for its many skyscrapers, and its economy continue to boom. It has one of the lowest tax-rates in the Asia-Pacific region, and continues to be open towards foreign direct investment (FDI). Moreover, Singapore has one of the highest trade-to-GDP ratios worldwide, underlining its export-oriented economy. Finally, its geographic location has given it a strategic position as a center connecting other countries in the region with the outside world. However, the economic boom has come at a cost, with the city now ranked among the world's most expensive.

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.

Asia Dragon Trust (DGN) Stock Forecast: Prepare to Soar with This Emerging Market Powerhouse

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 dataset provides values for LABOR MARKET CONDITIONS INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.