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.

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.

Browse LSEG's NASDAQ Europe real-time and delayed market data, covering all asset types such as equity, ETPs, fixed income and derivatives.

Germany's main stock market index, the DE40, fell to 24091 points on July 14, 2025, losing 0.68% from the previous session. Over the past month, the index has climbed 1.65% and is up 29.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on July of 2025.

Smart Insider’s Global Share Buyback Database offers invaluable insights to investors on stock market data. We provide detailed, up-to-date share buyback data covering over 55,000 companies globally and over 8,000+ in Europe & UK, that’s every company that reports Buybacks through regulatory processes.

Our Share buyback data includes detailed information on all major buyback transactions including source announcements and derived analysis fields. Our platform adds a visual representation of the data, allowing investors to quickly identify patterns and make decisions based on their findings.

Get detailed share buyback insights with Smart Insider and stay ahead of the curve with accurate, historical buyback insight that helps you make better investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as CSV, XML or XLSX via SFTP, API or Snowflake.

Sample dataset for Desktop Service has been provided with limited fields. Upon request, we can provide a detailed Quant sample.

Tags: Equity Market Data, Stock Market Data, Corporate Actions Data, Corporate Buyback Data, Company Financial Data, Insider Trading Data

The latest closing stock price for European Equity Fund as of June 25, 2025 is 10.00. An investor who bought $1,000 worth of European Equity Fund stock at the IPO in 1986 would have $6,590 today, roughly 7 times their original investment - a 5.33% compound annual growth rate over 39 years. The all-time high European Equity Fund stock closing price was 10.08 on June 12, 2025. The European Equity Fund 52-week high stock price is 10.18, which is 1.8% above the current share price. The European Equity Fund 52-week low stock price is 8.05, which is 19.5% below the current share price. The average European Equity Fund stock price for the last 52 weeks is 9.05. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

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.

P-values of two samples Kolmogorov-Smirnov test comparing real data distribution with q normal distribution for individual stocks and the whole WIG 30 index (independent fit of left and right tail is performed).

Euro Stoxx 50 is the index designed by STOXX, a globally operating index provider headquartered in Zurich, Switzerland, which in turn is owned by Deutsche Börse Group. This index provides the broad representation of the Eurozone blue chips performance. Blue chips are corporations known on the European market for quality, reliability and the ability to operate profitably both in good and bad economic times.
Development of the Euro Stoxx 50 index The year-end value of the Euro Stoxx 50 peaked in 1999, with 4,904.46 index points. It noted significant decrease between 1999 and 2002, then an increase to 4,399.72 in 2007, prior to the global recession. Since the very sharp decline in 2008, there was a tentative increase, never yet reaching the pre-recession levels. As of the end of 2021, the Euro Stoxx 50 index was getting close to its historical heights, reaching 4,298.41 points, its highest position post recession, before falling again in 2022. In 2023 and 2024, the index rose again, reaching 4,862.28 points. Some of the following reputable companies formed the Euro Stoxx 50 index: Adidas, Airbus Group, Allianz, BMW, BNP Paribas, L'Oréal, ING Group NV, Nokia, Phillips, Siemens, Société Générale SA or Volkswagen Group.
European financial stock exchange indices Other European indices include the DAX (Deutscher Aktienindex) index and the FTSE 100 (Financial times Stock Exchange 100 index). FTSE, informally known as the “Footsie”, is a share index of the 100 companies listed on the London Stock Exchange with the highest market capitalization. The Index, which began in January 1984 with the base level of 1,000, reached 7,733.24 at the closing of 2023. More in-depth information can be found in the report on stock market indices.

Skewness of price returns for chosen stokcs from WIG 30 stock index.

In 2022, the leading stock exchange in Europe in terms of IPOs size was the Frankfurt Stock Exchange (Deutsche Börse), with a value of 9.4 billion euros. The following two largest exchanges were the Borsa Italiana in Milan (part of Euronext Group), and the London Stock Exchange, with around 1.4 billion and 1.1 billion euros respectively.

This paper studies the heterogeneous effects of exchange rate and stock market on carbon emission allowance price in four emissions trading scheme pilots in China. We employ a panel quantile regression model, which can describe both individual and distributional heterogeneity. The empirical results illustrate that the effects of explanatory variables on carbon emission allowance price is heterogeneous along the whole quantiles. Specifically, exchange rate has a negative effect on carbon emission allowance price at lower quantiles, while becomes a positive effect at higher quantiles. In addition, a negative effect exists between domestic stock market and carbon emission allowance price, and the intensity decreasing along with the increase of quantile. By contrast, an increasing positive effect is discovered between European stock market and domestic carbon emission allowance prices. Finally, heterogeneous effects on carbon emission allowance price can also be proved in European Union Emission Trading Scheme (EU-ETS).

We compile all return and macroeconomic data from Kenneth French's website and the OECD statistical data warehouse, respectively, for the period from January 1990 to December 2018. All return and macroeconomic data include the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and United Kingdom.The dataset comprises the following series:

1. Fama-French factors, 3-factor model, as provided by Kenneth French (Europe_3_Factors.txt).
2. Fama-French factors, 5-factor model, as provided by Kenneth French (Europe_5_Factors.txt).
3. Returns for 25 size-BE/ME portfolios, as provided by Kenneth French (Europe_25_Portfolios_ME_BE-ME.txt).
4. Returns for 25 size-momentum, as provided by Kenneth French (Europe_25_Portfolios_ME_Prior_12_2.txt).
5. Weighted average per capita consumption growth. We first collect quarterly chained volume estimates for consumption in nondurables and services, non-seasonally adjusted, in national currency, for the 16 countries under consideration (‘Non-durable goods’ and ‘Services’ series, LNBQR measure). Second, we use the population series provided by the OECD to determine per capita consumption growth series for each country. Finally, we estimate the average consumption growth for the economies under consideration, weighting by population (Europe_Consumption_Q.txt).
6. Weighted average consumer confidence index (CCI). We collect monthly CCI data as provided by the OECD (‘OECD Standardised CCI, Amplitude adjusted, sa’ series, dataset ‘Composite Leading Indicators’, MEI). We determine the average CCI for the economies under consideration, weighting by population (Europe_Indicators_Q.txt).

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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.
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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

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.

Central Europe Equity: A Rising Star in Emerging Markets? (CEE)

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

Returns of the series used in the publication "Return connectedness between energy commodities and stock markets: New evidence from 31 energy sector companies in Europe" (Just M, Kliber A, Echaust K)

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.

Investcorp Europe Acquisition Emerging Global Champs? (IVCB)

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

Comprehensive dataset of 152 companies listed on Moscow Stock Exchange, including detailed financial information, market data, and corporate filings. This dataset provides real-time updates on trading metrics, company profiles, financial statements, regulatory filings, and market performance indicators. Updated every 30 minutes, it covers key data points such as market capitalization, trading volume, stock prices, company fundamentals, and regulatory compliance information for all listed securities on Moscow Stock Exchange.

Europe Current Sensor Market is poised to witness substantial growth, reaching a value of USD 46.54 Million by the year 2033, up from USD 24.22 Million attained in 2024. The market is anticipated to display a Compound Annual Growth Rate (CAGR) of 7.53% between 2025 and 2033.

The Europe Current Sensor Market size to cross USD 46.54 Million in 2033. [https://edison.valuemarketresearch.com//uploads/

This study examines how news is distributed across stocks. A model is developed that categorizes a stock's latent news into normal and nonnormal news, and allows both types of news to be filtered through to other stocks. This is achieved by formulating a model that jointly incorporates a multivariate lognormal-Poisson jump process (for nonnormal news) and a multivariate GARCH process (for normal news), in addition to a news (or shock) transmission mechanism that allows the shocks from both processes to impact intertemporally on all stocks in the system. The relationship between news and the expected volatility surface is explored and a unique news impact surface is derived that depends on time, news magnitude, and news type. We find that the effect of nonnormal news on volatility expectations typically builds up before dissipating, with the news transmission mechanism effectively crowding-out normal news and crowding-in nonnormal news. Moreover, in contrast to the standard approach for measuring leverage effects using asymmetric generalized autoregressive conditional heteroskedasticity models, we find that leverage effects stem predominantly from nonnormal news. Finally, we find that the capacity to identify positively or negatively correlated stock returns is ambiguous in the short term, and depends heavily on the behavior of the nonnormal news component.