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.

China's main stock market index, the SHANGHAI, rose to 3520 points on July 14, 2025, gaining 0.27% from the previous session. Over the past month, the index has climbed 3.86% and is up 18.35% 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.

The global securities brokerage and stock exchange services market is experiencing robust growth, driven by increasing retail investor participation, the expansion of online trading platforms, and the rise of algorithmic trading. The market's value, estimated at $5 trillion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $9 trillion by 2033. This growth is fueled by several key factors. Technological advancements are making trading more accessible and efficient, attracting a younger demographic of investors. The increasing adoption of mobile trading apps and sophisticated online platforms is further contributing to this trend. Moreover, the globalization of financial markets and the growing sophistication of investment strategies are driving demand for specialized brokerage services and sophisticated exchange infrastructure. Geopolitical events and economic shifts continue to create volatility and opportunity, attracting both institutional and individual investors. The market is segmented by application (exclusive brokers, banks, investment firms, others) and type (online, offline), with online brokerage experiencing the fastest growth. Major players like Goldman Sachs, JPMorgan Chase, and others dominate the market landscape, leveraging their established networks and technological capabilities. Regional variations exist, with North America and Asia-Pacific currently holding the largest market shares, though emerging markets in regions such as Africa and South America are poised for significant growth. Regulatory changes and cybersecurity concerns represent key challenges, though innovation and regulatory adaptation are expected to mitigate these risks. The competitive landscape is characterized by both intense competition and consolidation. Larger firms are acquiring smaller brokerage houses and technology companies to expand their offerings and geographic reach. This trend of consolidation is likely to continue as market participants seek to achieve economies of scale and enhance their technological capabilities. Furthermore, the increasing adoption of fintech solutions and artificial intelligence (AI) in trading and investment management is expected to reshape the competitive dynamics. This suggests a future where personalized services, AI-driven insights, and sophisticated risk management solutions become increasingly critical for success in this dynamic market. The continued focus on regulatory compliance, security, and client experience will remain paramount for all market participants.

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.

The global stock analysis software market is experiencing robust growth, driven by increasing adoption of algorithmic trading, rising retail investor participation, and the expanding use of advanced analytical tools. The market, currently valued at approximately $2.5 billion in 2025 (estimated based on typical market sizes for similar software segments and a logical extrapolation considering the provided CAGR), is projected to witness a Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033). Key segments driving this expansion include the banking, financial services, and insurance (BFSI) sector, alongside the rapidly growing healthcare, telecom, and IT industries. The preference for sophisticated fundamental and technical analysis tools is fueling demand, with evolutionary analysis gaining traction as a promising emerging segment. Regional dominance is currently held by North America, attributable to a mature financial market and high technology adoption. However, Asia Pacific is anticipated to exhibit the highest growth rate, fueled by increasing market awareness and expanding internet penetration. The market's expansion is further propelled by the rising availability of user-friendly, cloud-based stock analysis platforms. However, challenges remain. These include the high initial investment costs for advanced software and the potential for complexities in data interpretation for less experienced users. Nonetheless, innovative features such as AI-powered predictive analytics and integration with brokerage accounts are expected to mitigate these barriers and enhance market adoption. The competitive landscape is marked by both established players and emerging startups, leading to innovation and further driving market growth. Competitive differentiation is achieved through advanced features, user experience, and robust customer support. The consistent need for accurate, timely, and actionable insights ensures the continued importance of this sector in navigating global financial markets.

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.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

External debt stocks, total (DOD, current US$) in South Asia was reported at 961453754731 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - External debt stocks, total (DOD, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

External debt stocks, long-term (DOD, current US$) in South Asia was reported at 761351031804 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - External debt stocks, long-term (DOD, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on June 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.

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 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

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.

The market for Technical Analysis Tools for Traders is experiencing robust growth, driven by increasing retail investor participation, the proliferation of online trading platforms, and a growing preference for data-driven investment strategies. This market, estimated at $2.5 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key trends, including the rising adoption of cloud-based solutions offering accessibility and scalability, the increasing sophistication of indicators and algorithms incorporated into these tools, and the expansion of the market into emerging economies. The segmentation of the market into different application types (price indicators, support/resistance, momentum, volume, oscillators, statistical indicators) and deployment types (cloud-based, on-premise) reflects the diverse needs of traders across skill levels and trading styles. While data security concerns and the need for continuous updates and maintenance represent some challenges, the overall market outlook remains positive, driven by ongoing technological innovation and the persistent demand for tools that can enhance trading performance. The competitive landscape is characterized by a mix of established financial institutions offering integrated trading platforms and specialized technology providers focusing on advanced analytical tools. Key players like Trading Central, Ally Invest, Charles Schwab, and others are constantly innovating to improve their offerings, leading to increased market competition and driving further improvements in the quality and affordability of technical analysis tools. The geographic distribution of the market is broad, with North America currently holding a significant share, followed by Europe and Asia-Pacific. However, emerging markets in Asia and Latin America present significant growth opportunities as investor sophistication and online trading penetration increase in those regions. The forecast period anticipates continued expansion across all segments, driven by technological advancements and increasing adoption among both professional and retail traders globally.

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.

PAI PLATINUM ASIA INVESTMENTS 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

The global financial database market is experiencing robust growth, driven by the increasing demand for real-time data and advanced analytical capabilities across various sectors. The market's expansion is fueled by several key factors, including the rising adoption of sophisticated investment strategies, regulatory compliance needs, and the burgeoning fintech industry. The market is segmented by application (personal and commercial use) and database type (real-time and historical). Commercial use currently dominates the market, owing to the extensive data requirements of financial institutions, investment banks, and research firms. However, personal use is anticipated to witness significant growth driven by the increasing accessibility and affordability of financial data through online platforms and subscription services. The real-time database segment holds a larger market share due to its critical role in high-frequency trading and real-time risk management. Key players like Bloomberg, Refinitiv (formerly Thomson Reuters), and S&P Capital IQ are establishing themselves as market leaders through continuous product innovation and strategic acquisitions, solidifying their dominant positions through comprehensive data offerings and sophisticated analytical tools. Geographic expansion is another key driver, with regions like North America and Europe currently holding significant market share, while Asia Pacific is poised for substantial growth due to the expanding financial markets and increasing technological adoption in the region. Competitive pressures are evident, with several companies striving to differentiate themselves through specialized data offerings and partnerships. The forecast period (2025-2033) suggests continued market expansion, albeit at a potentially moderating CAGR compared to previous years. This moderation could be attributed to market saturation in some developed regions and the potential for economic fluctuations. However, emerging markets and technological advancements, such as AI-driven analytics and the integration of alternative data sources, will likely continue to fuel market growth. The increasing importance of ESG (environmental, social, and governance) factors in investment decisions is also expected to drive demand for specialized financial databases that incorporate such data. The ongoing evolution of data security and privacy regulations will also play a crucial role in shaping the market's trajectory. Maintaining data integrity and compliance will be critical for market players.

The global equity registration and transfer services market is experiencing robust growth, driven by increasing capital market activity, rising numbers of publicly listed companies, and the expanding adoption of digital platforms. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $25 billion by 2033. This growth is fueled by several key factors. Firstly, the ongoing surge in initial public offerings (IPOs) and secondary market transactions necessitates efficient and secure equity registration and transfer processes. Secondly, regulatory changes and increased compliance requirements are driving demand for sophisticated service providers capable of handling complex procedures. Thirdly, the increasing integration of technology, including blockchain and AI, is streamlining operations and enhancing security, leading to greater efficiency and cost reduction for market participants. However, challenges remain, including the need for continuous investment in technology infrastructure to keep pace with evolving market needs and the inherent risks associated with cybersecurity threats. The market is segmented by service type (equity registration and equity transfer) and application (issuer and investor). While both services are crucial, the equity registration segment currently holds a larger market share due to the regulatory obligations placed on issuers. However, the equity transfer segment is projected to witness faster growth in the coming years, propelled by increased trading activity and investor participation in global markets. Geographically, North America currently dominates the market, followed by Europe and Asia-Pacific. However, emerging markets in Asia-Pacific, particularly in India and China, are expected to demonstrate significant growth potential, contributing substantially to the overall market expansion in the forecast period. The competitive landscape features both large multinational corporations and specialized regional players, indicating a high level of market consolidation and specialization. Continued innovation in technology and strategic acquisitions will shape the future competitive dynamics of this sector.

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.

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.

Exchange Traded Fund Market Outlook

The global exchange traded fund (ETF) market size was estimated at approximately USD 9.3 trillion in 2023 and is projected to reach USD 20.7 trillion by 2032, growing at a compound annual growth rate (CAGR) of 9.2%. This impressive growth is driven by several factors including increasing demand for diversified investment portfolios, lower expense ratios compared to mutual funds, and enhanced market liquidity.

One of the primary growth factors for the ETF market is the increased awareness and understanding of ETFs among retail and institutional investors. ETFs provide a cost-effective way to access a broad array of asset classes and investment strategies, which has contributed significantly to their popularity. Additionally, the rise in digital trading platforms has made it easier for individual investors to buy and sell ETFs, further fueling market expansion. Institutional investors are also increasingly favoring ETFs for their flexibility and efficiency in portfolio management, which has driven volume and growth in the market.

Another significant growth driver is the continuous innovation within the ETF industry. New types of ETFs are being introduced regularly, including thematic ETFs focusing on emerging industries like artificial intelligence, renewable energy, and blockchain technology. These innovative products attract a diverse set of investors looking to capitalize on specific market trends or sectors. Furthermore, the development of active ETFs, which combine the benefits of active management with the liquidity and transparency of ETFs, has opened new avenues for growth.

The regulatory environment has also played a crucial role in the expansion of the ETF market. Regulatory bodies across various regions have provided a supportive framework that fosters the growth of ETFs. For example, the Securities and Exchange Commission (SEC) in the United States has streamlined the approval process for new ETFs, making it easier for asset managers to launch new products. Similar supportive measures have been witnessed in Europe and Asia, contributing to the global growth of the market.

Open Ended Funds Oef have been gaining traction as an alternative investment vehicle alongside ETFs. These funds offer investors the flexibility to enter and exit at their convenience, which is particularly appealing in volatile market conditions. Unlike ETFs, which trade on exchanges, Open Ended Funds Oef are priced at the end of the trading day based on their net asset value. This structure provides a different approach to liquidity and pricing, which can be advantageous for certain investment strategies. Investors looking for a more hands-on approach to fund management may find Open Ended Funds Oef to be a suitable option, as they often allow for more active management compared to the passive nature of many ETFs. The growing interest in these funds highlights the diverse range of investment products available to meet varying investor needs and preferences.

Regionally, North America holds the largest share of the ETF market, driven by strong market adoption in the United States and Canada. The presence of well-established financial markets and high investor awareness contribute to this dominance. Europe is another significant market, with increasing ETF adoption in countries like Germany, the United Kingdom, and France. The Asia Pacific region is experiencing rapid growth, particularly in countries like China, Japan, and Australia, due to rising financial literacy and growing investment in equities. The Middle East & Africa, while currently a smaller market, is witnessing gradual growth driven by economic reforms and increasing interest in diversified investment options.

Type Analysis

Equity ETFs represent the largest segment within the ETF market. These funds invest in stocks and aim to replicate the performance of an underlying equity index, such as the S&P 500. The appeal of equity ETFs lies in their ability to offer broad market exposure, diversification, and relatively low cost. Investors are increasingly gravitating towards equity ETFs to capitalize on market growth and potential capital appreciation. The robust performance of stock markets globally has further fueled the demand for equity ETFs, making them a cornerstone of many investment portfolios.

Bond ETFs are another significant segment, providing exposure to fixed-income securities such as government and corporate bonds