Euro Area's main stock market index, the EU50, fell to 5289 points on July 4, 2025, losing 1.03% from the previous session. Over the past month, the index has declined 2.25%, though it remains 6.21% higher than a year ago, 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.

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.

France's main stock market index, the FR40, fell to 7696 points on July 4, 2025, losing 0.75% from the previous session. Over the past month, the index has declined 1.21%, though it remains 0.27% higher than a year ago, 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.

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.

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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.

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

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/

The latest closing stock price for European Wax Center as of June 13, 2025 is 5.74. An investor who bought $1,000 worth of European Wax Center stock at the IPO in 2021 would have $-700 today, roughly -1 times their original investment - a -25.99% compound annual growth rate over 4 years. The all-time high European Wax Center stock closing price was 29.56 on October 28, 2021. The European Wax Center 52-week high stock price is 11.21, which is 95.3% above the current share price. The European Wax Center 52-week low stock price is 2.72, which is 52.6% below the current share price. The average European Wax Center stock price for the last 52 weeks is 6.40. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

This study examines the long-run price relationship and the dynamic price transmission among the USA, Germany, and four major Eastern European emerging stock markets, with particular attention to the impact of the 1998 Russian financial crisis. The results show that both the long-run price relationship and the dynamic price transmission were strengthened among these markets after the crisis. The influence of Germany became noticeable on all the Eastern European markets only after the crisis but not before the crisis. We also conduct a rolling generalized VAR analysis to confirm the robustness of the main findings.

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).

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 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

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

The size of the Wealth Management Industry in Europe market was valued at USD 43.02 Million in 2023 and is projected to reach USD 58.19 Million by 2032, with an expected CAGR of 4.41% during the forecast period. The wealth management industry encompasses a range of financial services designed to assist individuals and families in managing their financial assets and achieving their long-term financial goals. This industry primarily targets high-net-worth individuals (HNWIs) and ultra-high-net-worth individuals (UHNWIs), offering personalized services that include investment management, financial planning, tax advice, estate planning, and retirement planning. Wealth management firms aim to provide a holistic approach to wealth accumulation and preservation, tailoring strategies to meet the unique needs and preferences of their clients. As the global economy evolves, the wealth management industry is experiencing significant growth driven by increasing wealth concentrations, particularly in emerging markets. The rise in disposable income, along with the growing awareness of the importance of financial planning, has led to a greater demand for comprehensive wealth management services. Additionally, technological advancements, such as robo-advisors and financial technology (fintech) platforms, are transforming how wealth management services are delivered, making them more accessible and efficient. Recent developments include: September 2022: UBS was set to acquire the Millennial and Gen Z-focused Wealthfront. UBS and wealth management platform Wealthfront have pulled out of a proposed acquisition deal., 2021: L&G launched the next-gen protection platform for IFAs. Legal & General Group Protection has launched a next-generation online quote-and-buy platform to widen access to group income protection. The insurer states that its Online Insurance Experience (ONIX) aims to create more digital opportunities for intermediaries to support their clients' needs for life cover. ONIX is designed to deliver a quote experience that is more flexible with increased options that focus on capturing the client's specific requirements. The launch of ONIX is accompanied by the insurer's new 'Big on small business' SME Group Protection sales materials.. Key drivers for this market are: Guaranteed Protection Drives The Market. Potential restraints include: Long and Costly Legal Procedures. Notable trends are: Growth In Millionaire Wealth Leading to the European Wealth Management Market Uptrend.

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).

The European SOCaaS market is projected to reach a value of approximately USD 3.14 billion by 2033, expanding at a CAGR of roughly 14.85% during the forecast period of 2025-2033. The market witnessed a value of approximately USD 1.08 billion in 2025. The growing adoption of cloud-based security solutions, the increasing need for threat detection and response capabilities, and the rising awareness of cybersecurity risks are the key factors driving the growth of the market. Furthermore, the increasing adoption of digital transformation initiatives across various industries, such as BFSI, healthcare, and manufacturing, is also contributing to the market growth. The European SOCaaS market is highly fragmented, with a number of global and regional players operating in the market. Some of the key players in the market include Lumen Technologies, Sophos Ltd., Thales, Wipro, Atos SE, Cloudflare Inc., ConnectWise LLC, Teceze Limited, Ontinue Inc., and PlusServer. These players are focusing on expanding their geographical presence, introducing new products and services, and forming strategic partnerships to gain a competitive edge in the market. The market is also witnessing the emergence of new players, which is expected to intensify competition in the coming years. Recent developments include: January 2024 - The cloud computing and analytics supplier for the world's financial markets, Beaks Group, partnered with BlueVoyant, a cybersecurity company that identifies, verifies, and addresses internal and external threats. Beeks group will receive BlueVoyant's renowned managed extended detection and response (MXDR) services, which boost operational resilience and security. Beeks will provide improved cloud security solutions for the banking industry by utilizing BlueVoyant solution as part of its current Microsoft technology infrastructure; Beeks will be able to run an around-the-clock comprehensive security operations center (SOC) with the aid of BlueVoyant services., November 2023 - Infosys, a provider of cybersecurity services in the BFSI sector, unveiled its new proximity center in Sofia, Bulgaria, as part of its European expansion. The center will provide an ideal ecosystem for companies across the financial services sector. This proximity center is positioned to assist global and European customers in accelerating AI and Cloud-led digital journeys, particularly in the financial services sector. The company is committed to building a resilient cybersecurity program to increase operational efficiency and reduce costs. The expansion is a company's strategic move in security operation centers (SOC), AI and ML-based integrated cybersecurity platforms, and partnerships.. Key drivers for this market are: Rise in the Adoption of Pay-per-use Model Owing to Reduction in Capex, Rapid Adoption of Cloud Deployment in SMEs; Mobile Workforce and Associated Vulnerabilities. Potential restraints include: Challenges Associated With Data Control and Total Cost of Ownership. Notable trends are: Retail and Consumer Goods to be the Fastest Growing End-user Industry.

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.

CEE The Central and Eastern Europe Fund Inc. (The) Common Stock

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 Western Europe ETF market report offers a thorough competitive analysis, mapping key players’ strategies, market share, and business models. It provides insights into competitor dynamics, helping companies align their strategies with the current market landscape and future trends.

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.

EUR EUROPEAN LITHIUM LIMITED

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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

Indo-European Do It Yourself (IDOX): A Stock Worth Watching?

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.

Starwood European Real Estate Finance (SWEF) Stock Forecast: Hold On Tight for a Wild Ride!

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