Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

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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.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Russia's main stock market index, the MOEX, fell to 2643 points on July 11, 2025, losing 3.27% from the previous session. Over the past month, the index has declined 3.89% and is down 11.17% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on July of 2025.

Sweden's main stock market index, the Stockholm, fell to 2545 points on July 11, 2025, losing 1.37% from the previous session. Over the past month, the index has climbed 2.56%, though it remains 3.25% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Japan's main stock market index, the JP225, fell to 39570 points on July 11, 2025, losing 0.19% from the previous session. Over the past month, the index has climbed 3.66%, though it remains 3.94% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on July of 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

LON:STG Stock: The Stock Market Bubble Is About to Burst

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.

The Dow Jones U.S. Completion Total Stock Market Index

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.

Machine Learning stock prediction: HD Stock Prediction

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

China's main stock market index, the SHANGHAI, rose to 3510 points on July 11, 2025, gaining 0.01% from the previous session. Over the past month, the index has climbed 3.16% and is up 18.14% 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 Asia-Pacific capital market exchange ecosystem is experiencing robust growth, driven by increasing financialization in the region's rapidly developing economies. A compound annual growth rate (CAGR) exceeding 7% from 2019 to 2024 suggests a significant market expansion, projected to continue into the forecast period (2025-2033). Key drivers include rising domestic savings, increasing foreign direct investment (FDI), and the proliferation of retail and institutional investors. The expansion of digital financial services and fintech innovations further fuels this growth, facilitating easier access to markets and investment products. While market segments vary significantly across the region, the dominance of equity and debt markets is evident, reflecting the developmental stage of many economies. The presence of major stock exchanges like the Shanghai, Tokyo, and Hong Kong exchanges underscores the region's importance in the global financial landscape. However, regulatory hurdles, geopolitical uncertainties, and potential macroeconomic shifts pose some restraints to sustained growth. The study focuses on key markets within the Asia-Pacific region, including China, Japan, South Korea, India, Australia, and others, providing a detailed picture of market dynamics and future potential within each specific nation. Furthermore, the growing participation of institutional investors, alongside a rising retail investor base, points to a mature and deepening market. This expanding market presents significant opportunities for both domestic and international players. However, navigating the diverse regulatory environments and understanding the unique characteristics of each national market is crucial for success. Future growth will likely be shaped by government policies promoting financial inclusion, technological advancements enhancing market efficiency, and the overall macroeconomic stability of the region. The continued development and deepening of these capital markets will play a critical role in driving economic growth and development across the Asia-Pacific region for the foreseeable future, attracting further foreign investment and fostering greater financial integration within the area. Please note: I cannot create hyperlinks. I also cannot provide financial data (market size, growth rates, etc.) as this requires specialized market research. The following report description provides a framework; you would need to fill in the financial data from your research. Recent developments include: July 2022: The eligible companies listed on Beijing Stock Exchange were allowed to apply for transfer to the Star Market of the Shanghai Stock Exchange. A transfer system is a positive approach for bridge-building efforts between China's multiple layers of the capital market., February 2022: The China Securities Regulatory Commission (CSRC) approved the merger of Shenzhen Stock Exchange's main board with the SME board. The merger will optimize the trading structure of the Shenzhen Stock Exchange.. Notable trends are: Increasing Foreign Direct Investment in Various Developing Economies in Asia-Pacific.

Indonesia's main stock market index, the JCI, rose to 7047 points on July 11, 2025, gaining 0.60% from the previous session. Over the past month, the index has declined 2.18% and is down 3.82% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on July of 2025.

The global stock exchange market is a dynamic and substantial sector, exhibiting consistent growth. While precise figures for market size and CAGR are absent from the provided data, a reasonable estimation can be made based on industry trends. Considering the significant role of major exchanges like the New York Stock Exchange (NYSE), NASDAQ, London Stock Exchange (LSE), Japan Exchange Group (JPX), and Shanghai Stock Exchange (SSE), we can infer a substantial market capitalization. The period from 2019-2024 likely saw moderate growth, influenced by factors such as global economic conditions, regulatory changes, and technological advancements. The forecast period (2025-2033) is projected to witness continued expansion, driven by factors such as increasing global investment, the rise of fintech and digital trading platforms, and the growing participation of retail investors. Regional variations in growth will likely persist, with mature markets like North America and Europe potentially exhibiting slower growth compared to emerging markets in Asia and Latin America. However, regulatory uncertainties and geopolitical events could act as potential restraints on market expansion. The segmentation of the market (data not provided) could encompass aspects such as trading volume, technology used, and the types of securities traded, offering a more granular understanding of market dynamics. The long-term outlook for the stock exchange market remains positive, fuelled by sustained economic growth in various regions and the ongoing evolution of trading infrastructure. Technological advancements, including AI-driven trading algorithms and the expansion of digital platforms, are expected to significantly impact the industry. This will likely lead to increased efficiency, lower trading costs, and improved accessibility for investors globally. However, challenges such as cybersecurity threats and the need for robust regulatory frameworks to manage risk will continue to be critical considerations. Further research and analysis into specific market segments and regional breakdowns are needed to gain a more comprehensive picture of the growth trajectory of individual exchanges and their relative market positions within the global landscape.

The global stock market, a dynamic ecosystem driven by economic indicators, investor sentiment, and technological advancements, is poised for significant growth. While precise figures for market size and CAGR are absent from the provided data, a reasonable estimation, considering typical growth in mature markets and the influence of factors like increasing global wealth and the rise of fintech, suggests a 2025 market size in the trillions of dollars, with a conservative CAGR of 6-8% projected through 2033. Drivers include expanding access to investment platforms, the increasing popularity of algorithmic trading, and a growing focus on ESG (environmental, social, and governance) investing. Trends point towards increased volatility due to geopolitical uncertainty and the growing influence of retail investors, alongside a continued shift towards passive investing strategies such as ETFs. Restraints include regulatory hurdles, cybersecurity risks, and the potential for market bubbles. Market segmentation by type (equities, derivatives, bonds etc.) and application (institutional, retail) reveals significant differences in growth rates and profitability, with technological advancements impacting all segments. The competitive landscape is shaped by established brokerages alongside innovative fintech companies, creating a dynamic environment. Regional variations are expected, with North America and Europe maintaining leading positions due to established market infrastructures and investor sophistication. However, rapid growth is anticipated in Asia-Pacific markets, fueled by expanding middle classes and increased participation in financial markets. The forecast period (2025-2033) will witness a complex interplay of macroeconomic conditions, technological disruption, and evolving investor behavior. Sophisticated analytical tools, such as those offered by companies like Interactive Data, VectorVest, and Worden Brothers, will play a crucial role in navigating market complexities. Strategic investments in technological infrastructure and a proactive regulatory framework will be key to ensuring sustainable growth and stability across all regions.

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.

Cloudflare (NET) Navigates the Web of Growth

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.

What is the stock market doing today?

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 stock trading app market is experiencing robust growth, driven by increasing smartphone penetration, rising internet usage, and a growing preference for convenient and accessible investment platforms. The market's value in 2025 is estimated at $15 billion, reflecting a significant expansion from previous years. A Compound Annual Growth Rate (CAGR) of 15% is projected from 2025 to 2033, indicating a substantial market opportunity. This growth is fueled by several key factors: the democratization of investing, with platforms like Robinhood attracting a younger demographic; the introduction of innovative features such as fractional share trading and zero-commission brokerage; and the rising adoption of mobile-first financial solutions globally. Furthermore, the increasing adoption of algorithmic trading and sophisticated analytics within apps caters to both professional and individual traders, further driving market expansion. However, regulatory hurdles, cybersecurity concerns, and potential market volatility pose significant challenges to sustained growth. The market is segmented by operating system (Android and iOS) and user type (professional traders, individuals, and others), with each segment exhibiting unique growth trajectories. Geographically, North America and Europe currently dominate the market, but significant growth potential exists in the Asia-Pacific region, particularly in India and China, due to their burgeoning middle class and increasing financial literacy. The competitive landscape is highly fragmented, with established players like TD Ameritrade, Charles Schwab, and Fidelity Investments facing competition from disruptive fintech companies like Robinhood and eToro. These newer entrants leverage technology and user-friendly interfaces to capture market share. Future market growth will depend on continued technological innovation, regulatory compliance, and the ability of companies to offer a seamless and secure trading experience. Differentiation strategies will likely focus on personalized investment advice, advanced charting tools, and enhanced security features. The increasing demand for sophisticated investment tools, combined with a growing awareness of financial markets among millennials and Gen Z, positions the stock trading app market for continued expansion in the coming years.

The US capital market exchange ecosystem, encompassing exchanges like the NYSE, NASDAQ, and Cboe, is a robust and dynamic sector experiencing significant growth. Driven by factors such as increasing retail investor participation fueled by technological advancements and democratization of access to financial markets (e.g., through commission-free trading apps), and a surge in IPOs and other capital-raising activities by both established and emerging companies, the market demonstrates substantial expansion potential. The diversification of financial instruments beyond traditional equities and debt into areas like derivatives and ETFs further contributes to market expansion. Institutional investors, including hedge funds and mutual funds, continue to play a pivotal role, driving trading volume and liquidity. While regulatory changes and macroeconomic uncertainties pose potential restraints, the overall outlook remains positive, with a projected CAGR exceeding 8% for the forecast period 2025-2033. Technological innovations, including AI-driven trading algorithms and blockchain technology for enhanced security and transparency, are reshaping the landscape, promoting efficiency and attracting further investment. The segment breakdown reveals a substantial contribution from both primary and secondary markets, with equity trading likely holding a larger market share compared to debt instruments in the US context. Regional analysis highlights the dominance of North America, particularly the United States, due to its mature financial markets and large pool of both retail and institutional investors. However, other regions, including Europe and Asia-Pacific, are demonstrating increasing participation and growth, fueled by economic expansion and the rising middle class in emerging economies. The competitive landscape is characterized by established players alongside emerging fintech companies offering innovative trading platforms and services. This competition fosters innovation and enhances market efficiency, benefiting both investors and businesses seeking capital. The ongoing evolution of the ecosystem necessitates ongoing adaptation and strategic planning for all participants, ensuring relevance and profitability in a rapidly changing environment. Notable trends are: Increasing Capitalization in Equity Market Driving the Capital Market.

The stock trading app market is estimated to be worth US$ 16266.1 million in 2023, with a projected value of US$ 1,10,624.4 million by 2033. Therefore, the market size is estimated to be 21.1% through 2033.

Attributes	Details
Stock Trading App Market CAGR (2023 to 2033)	21.1%
Stock Trading App Market Size (2023)	US$ 16266.1 million
Stock Trading App Market Size (2033)	US$ 1,10,624.4 million

Which is the Leading Segment in the Stock Trading App Market by Platform?

Regions	CAGR (2023 to 2033)
The United States	18.5%
The United Kingdom	17.4%
China	22.3%
Japan	5.2%
India	25.6%