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.

Can we predict stock market using machine learning? (WY Stock Forecast)

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

Bermuda's main stock market index, the BSX, closed flat at 2812 points on July 10, 2025. Over the past month, the index has declined 0.64%, though it remains 12.58% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Bermuda. Bermuda Stock Exchange Index - values, historical data, forecasts and news - updated on July of 2025.

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Securities Exchanges Market Size 2025-2029

The securities exchanges market size is forecast to increase by USD 56.67 billion at a CAGR of 12.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for investment opportunities. This trend is fueled by a global economic recovery and a rising interest in various asset classes, particularly in emerging markets. Another key driver is the increasing focus on sustainable and environmental, social, and governance (ESG) investing. This shift reflects a growing awareness of the importance of long-term value creation and the role of exchanges in facilitating socially responsible investments. This trend is driven by the expanding securities business units, including stocks, bonds, mutual funds, and other securities, which cater to the needs of investment firms and individual investors. However, the market is not without challenges. Increasing market volatility poses a significant risk for exchanges and their clients.
Furthermore, the rapid digitization of trading and the emergence of alternative trading platforms are disrupting traditional exchange business models. To navigate these challenges, exchanges must adapt by investing in technology, expanding their product offerings, and building strong regulatory frameworks. Data analytics and big data are also crucial tools for e-brokerage firms to gain insights and make informed decisions. By doing so, they can capitalize on the market's growth potential and maintain their competitive edge. Geopolitical tensions, economic instability, and regulatory changes can all contribute to market fluctuations and uncertainty.

What will be the Size of the Securities Exchanges Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, financial instrument classification plays a crucial role in facilitating efficient trade matching through advanced execution quality metrics and order book liquidity. Quantitative trading models leverage options clearing corporation data to optimize portfolio holdings, while trade matching engines utilize high-speed data storage solutions and portfolio optimization algorithms to minimize latency and enhance market depth indicators. Data center infrastructure and network bandwidth capacity are essential components for supporting complex algorithmic trading strategies, including latency reduction and price volatility forecasting. Market impact measurement and risk assessment methodologies are integral to managing market impact and mitigating fraud, ensuring regulatory compliance through transaction reporting standards and regulatory compliance software.

Exchange traded funds (ETFs) have gained popularity, necessitating robust quote dissemination systems and trade surveillance analytics. Server virtualization and cybersecurity threat mitigation strategies further strengthen the market's resilience, enabling seamless integration of data-driven quantitative models and sophisticated fraud detection algorithms. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data.

How is this Securities Exchanges Industry segmented?

The securities exchanges industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Market platforms
  Capital access platforms
  Others


Trade Finance Instruments

  Equities
  Derivatives
  Bonds
  Exchange-traded funds
  Others


Type

  Large-cap exchanges
  Mid-cap exchanges
  Small-cap exchanges


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Switzerland
    UK


  APAC

    China
    Hong Kong
    India
    Japan


  Rest of World (ROW)

By Service Insights

The Market platforms segment is estimated to witness significant growth during the forecast period. The market is characterized by advanced technologies and systems that enable efficient price discovery, manage settlement risk, and ensure regulatory compliance. Market platforms, which include trading platforms, order-matching systems, and market data dissemination, hold the largest share of the market. These platforms facilitate the buying and selling of securities, providing market liquidity and transparency. Real-time market surveillance and high-frequency trading infrastructure are crucial components, ensuring fair and orderly markets and enabling efficient trade execution. Financial modeling techniques and algorithmic trading platforms optimize trading strategies, while electronic communication networks and central counterparty cleari

Israel's main stock market index, the TA-125, fell to 3051 points on July 13, 2025, losing 2.22% from the previous session. Over the past month, the index has climbed 12.37% and is up 48.25% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - 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.

Should You Buy, Sell, or Hold? (AEX Index Stock Forecast)

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

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

France's main stock market index, the FR40, fell to 7829 points on July 11, 2025, losing 0.92% from the previous session. Over the past month, the index has climbed 0.83% and is up 1.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - 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.

Tadawul All Share index expected to show moderate gains.

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 foreign exchange (Forex) market is a global decentralized market for the trading of currencies. It is the largest financial market in the world, with an average daily trading volume of over $5 trillion. The market size is expected to reach $84 million by 2033, growing at a CAGR of 5.83% during the forecast period 2025-2033. Key drivers of the Forex market growth include increasing international trade, rising foreign direct investment, and growing demand for hedging and speculation. The market is also being driven by the increasing use of online trading platforms and the growing popularity of cryptocurrencies. The major players in the Forex market include Deutsche Bank, UBS, JP Morgan, State Street, XTX Markets, Jump Trading, Citi, Bank of New York Mellon, Bank America, and Goldman Sachs. The market is segmented by type (spot Forex, currency swap, outright forward, Forex swaps, Forex options, other types), counterparty (reporting dealers, other financial institutions, non-financial customers), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). Recent developments include: In November 2023, JP Morgan revealed the introduction of novel FX Warrants denominated in Hong Kong dollars in the Hong Kong market, marking its status as the inaugural issuer in Asia to present FX Warrants featuring CNH/HKD (Chinese Renminbi traded outside Mainland China/Hong Kong dollar) and JPY/HKD (Japanese Yen/Hong Kong dollar) as underlying currency pairs. These fresh FX Warrants are set to commence trading on the Hong Kong Stock Exchange., In October 2023, Deutsche Bank AG finalized its purchase of Numis Corporation Plc. The integration of both brands under the name 'Deutsche Numis' underscores their collective influence and standing in the UK and global markets. 'Deutsche Numis' emerges as a prominent entity in UK investment banking and the preferred advisor for UK-listed companies. This acquisition aligns with Deutsche Bank's Global Hausbank strategy, aiming to become the primary partner for clients in financial services and fostering stronger relationships with corporations throughout the United Kingdom., In June 2023, UBS successfully finalized the acquisition of Credit Suisse, marking a significant achievement. Credit Suisse Group AG has merged into UBS Group AG, forming a unified banking entity.. Key drivers for this market are: International Transactions Driven by Growing Tourism Driving Market Demand, Market Liquidity Impacting the Foreign Exchange Market. Potential restraints include: International Transactions Driven by Growing Tourism Driving Market Demand, Market Liquidity Impacting the Foreign Exchange Market. Notable trends are: FX Swaps is leading the market.

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.

Global Securities Brokerage And Stock Exchange market size 2025 was XX Million. Securities Brokerage And Stock Exchange Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

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.

FTSE 100: Where to Next?

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

Saudi Arabia PE Ratio: Tadawul: Energy data was reported at 15.870 NA in Nov 2018. This records an increase from the previous number of 13.927 NA for Oct 2018. Saudi Arabia PE Ratio: Tadawul: Energy data is updated monthly, averaging 14.450 NA from Jan 2017 (Median) to Nov 2018, with 23 observations. The data reached an all-time high of 20.053 NA in Sep 2017 and a record low of 8.730 NA in Jul 2017. Saudi Arabia PE Ratio: Tadawul: Energy data remains active status in CEIC and is reported by Tadawul. The data is categorized under Global Database’s Saudi Arabia – Table SA.Z014: Tadawul Stock Exchange: Price Earnings Ratio.

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.

Should I Buy Stocks Now or Wait Amid Such Uncertainty? (Jakarta Stock Exchange Composite Index 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

Spain's main stock market index, the ES35, fell to 14009 points on July 11, 2025, losing 0.94% from the previous session. Over the past month, the index has declined 0.57%, though it remains 24.52% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on July of 2025.

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Stockbroking Market Size 2025-2029

The stockbroking market size is forecast to increase by USD 27.45 billion at a CAGR of 10.1% between 2024 and 2029.

The market is characterized by the increasing need for real-time investment monitoring and surveillance, driven by heightened market volatility and investor demand for transparency. This trend is further fueled by advancements in technology, enabling brokerages to offer more sophisticated trading platforms and tools. The integration of artificial intelligence (AI) and algorithms into trading platforms has led to cloud-based solutions, enabling active and passive portfolio management. However, the market faces significant challenges, primarily due to the ongoing trade war and its associated economic uncertainties. The escalating tensions have led to increased market volatility and investor risk aversion, potentially dampening trading volumes and investor confidence.
As a result, stockbrokers must adapt to these market dynamics by offering innovative solutions that mitigate risk and provide value-added services to attract and retain clients. To capitalize on opportunities and navigate challenges effectively, companies should focus on enhancing their technology offerings, expanding their geographical reach, and developing strategic partnerships to stay competitive in this dynamic market. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data. 

What will be the Size of the Stockbroking Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the dynamic market, order routing optimization plays a crucial role in maximizing execution efficiency. Business continuity planning is essential to ensure uninterrupted services during crises. Financial statement analysis and performance attribution models help assess investment strategy implementation and identify areas for improvement. Data visualization tools facilitate effective operational risk management by providing insights into trading algorithms' performance. Backtesting methodologies and execution quality metrics are integral to refining quantitative trading models and derivatives pricing models. Futures trading strategies and disaster recovery planning are essential components of risk appetite modeling, enabling firms to manage volatility and mitigate potential losses. The stockbroking industry is essential for the smooth functioning of financial analytics.

Trade blotter reconciliation and client communication channels are vital for maintaining transparency and trust in client relationships. Portfolio construction strategies, financial reporting standards, and investment strategy implementation require a deep understanding of various regulatory requirements, including anti-money laundering (AML) and regulatory technology solutions. Algorithmic trading performance and account opening procedures are subject to continuous monitoring and optimization. Information security management and tax reporting compliance are essential aspects of maintaining a robust and compliant stockbroking business. Options trading strategies and transaction cost reduction are critical elements of a well-rounded investment offering.

How is this Stockbroking Industry segmented?

The stockbroking industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Mode Of Booking

  Offline
  Online


Type

  Long term trading
  Short term trading


End-user

  Institutional investor
  Retail investor


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Mode Of Booking Insights

The Offline segment is estimated to witness significant growth during the forecast period. Offline stockbroking is the traditional method of engaging in stock trading activities without the use of online platforms or electronic systems. Investors work with stockbrokers who act as an intermediary between them and the stock exchange. Offline stockbroking includes: Communication: Investors place their buy or sell orders through direct communication via calls, emails, or in person with their stockbrokers. Offline is still dominating the market due to the ease of use due to factors such as personalized services, extensive research, complex investment strategies, trust, and relationship building by the investors over time, also in the offline segment they can access initial public offerings or other restricted offerings which may not be readily available on an online brokera

Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

Use our Yahoo Finance Business Information 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.

Dataset Features

• name: Represents the company name.
• company_id: Unique identifier assigned to each company.
• entity_type: Denotes the type/category of the business entity.
• summary: A brief description or summary of the company.
• stock_ticker: The ticker symbol used for trading on stock exchanges.
• currency: The currency in which financial values are expressed.
• earnings_date: The date for the reported earnings.
• exchange: The stock exchange on which the company is listed.
• closing_price: The final stock price at the end of the trading day.
• previous_close: The stock price at the close of the previous trading day.
• open: The price at which the stock opened for the trading day.
• bid: The current highest price that a buyer is willing to pay for the stock.
• ask: The current lowest price that a seller is willing to accept.
• day_range: The range between the lowest and highest prices during the trading day.
• week_range: A broader price range over the past week.
• volume: Number of shares that traded in the session.
• avg_volume: Average daily share volume over a specific period.
• market_cap: Total market capitalization of the company.
• beta: A measure of the stock's volatility in comparison to the market.
• pe_ratio: Price-to-earnings ratio for valuation.
• eps: Earnings per share.
• dividend_yield: Dividend yield percentage.
• ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
• target_est: The analyst's target price estimate.
• url: The URL to more detailed company information.
• people_also_watch: Companies frequently watched alongside this company.
• similar: Other companies with similar profiles.
• risk_score: A quantified risk score.
• risk_score_text: A textual interpretation of the risk score.
• risk_score_percentile: The risk score expressed in percentile terms.
• recommendation_rating: Analyst recommendation ratings.
• analyst_price_target: Analyst provided stock price target.
• company_profile_address: Company address from the profile.
• company_profile_website: URL for the company’s website.
• company_profile_phone: Contact phone number.
• company_profile_sector: The sector in which the company operates.
• company_profile_industry: Industry classification of the company.
• company_profile_employees: Number of employees in the company.
• company_profile_description: A detailed profile description of the company.
• valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
• Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
• financials: This column appears to provide financial statement data.
• financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
• earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
• revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
• earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
• eps_trend: Contains information on how the EPS has trended over various recent time intervals.
• eps_revisions: Captures recent changes in EPS forecasts.
• growth_estimates: Offers projections related to growth prospects over different time horizons.
• top_analysts: Intended to list the top analysts covering the company.
• upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
• recent_news: Meant to contain recent news articles related to the company.
• fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
• **company_profile_he

South Korea's main stock market index, the KOSPI, fell to 3176 points on July 11, 2025, losing 0.23% from the previous session. Over the past month, the index has climbed 8.76% and is up 11.16% 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 July of 2025.

India's main stock market index, the SENSEX, fell to 82500 points on July 11, 2025, losing 0.83% from the previous session. Over the past month, the index has climbed 0.99% and is up 2.46% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Euro Area's main stock market index, the EU50, fell to 5350 points on July 14, 2025, losing 0.62% from the previous session. Over the past month, the index has climbed 0.19% and is up 7.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.