Euro Area's main stock market index, the EU50, rose to 5428 points on June 6, 2025, gaining 0.39% from the previous session. Over the past month, the index has climbed 3.78% and is up 7.45% 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 June 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.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around 40 percent of their value compared to January 5, 2020. However, Asian markets and the NASDAQ Composite Index only shed around 20 to 25 percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around 65 percent higher than in January 2020, while most other markets were only between 20 and 40 percent higher.

Why did the NASDAQ recover the quickest?

Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide.

Which markets suffered the most?

The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

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

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).

The data set contains material to replicate: Beck K, Stanek P (2019) Globalization or regionalization of stock markets? The case of Central and Eastern European Countries. Eastern European Economics, 57(4), 317-330. doi: https://doi.org/10.1080/00128775.2019.1610895 The data comprises stock market returns time series at weekly frequency between January 2000 and December 2018 on 44 stock price indices grouped into 11 sets corresponding to (1) East Asian and Australian developed markets, (2) “Chinese” markets (including Taiwan and Hong Kong), (3) “core” euro area, (4) “peripheral” euro area, (5) developed European markets outside the euro area, (6) V-4 countries, (7) “frontier” European markets (Russia, Turkey, Ukraine), (8) Baltic countries, (9) Latin American markets, (10) North American markets and (11) emerging South-East Asian countries. Data were retrieved from stooq.com and in case of some missing points, for example, due to Chinese New Year celebrations, log-linear interpolation was applied.

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

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

The report covers Europe Gas Companies and the market is Segmented by Application (Utilities, Industrial, and Commercial) and Geography (Germany, United Kingdom, France, Italy, Spain, and Rest of Europe). The market size and forecasts are provided in terms of production capacity in billion cubic meters for all the above segments.

Empirical result of European market.

The Europe Beer Market Report is Segmented by Product Type (Ale, Lager, and Other Product Types), Distribution Channel (On-Trade and Off-Trade), and Geography (Spain, United Kingdom, Germany, Russia, Italy, France, and the Rest of Europe). The Report Offers the Market Size in Value Terms in USD for all the Abovementioned Segments.

This dataset is about books, has 1 rows and is filtered where the book is European financial markets and institutions. It features 7 columns including book, author, publication date, language, and book publisher. The preview is ordered by publication date (descending).

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

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.

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

The Europe Marine Lubricants Market report segments the industry into Base Stock (Mineral Oil, Synthetic Oil, Bio-Based Oils), Product Type (System Oil, Marine Cylinder Lubricants, Trunk Piston Engine Oil, Gear Oil, Greases, Hydraulic Fluids, Other Product Types (Compressor and Refrigeration Oils, Steam Turbine Oils, etc.)), and Geography (Germany, United Kingdom, Italy, France, Spain, Rest of Europe).

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

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

Success.ai’s Company Financial Data for European Financial Professionals provides a comprehensive dataset tailored for businesses looking to connect with financial leaders, analysts, and decision-makers across Europe. Covering roles such as CFOs, accountants, financial consultants, and investment managers, this dataset offers verified contact details, firmographic insights, and actionable professional histories.

With access to over 170 million verified professional profiles, Success.ai ensures your outreach, market research, and partnership strategies are driven by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is indispensable for navigating the fast-paced European financial landscape.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of financial professionals across Europe.
   • AI-driven validation ensures 99% accuracy, reducing communication inefficiencies and improving engagement rates.

2. Comprehensive Coverage Across Europe

   • Includes financial professionals from key markets such as the United Kingdom, Germany, France, Italy, and the Netherlands.
   • Gain insights into regional financial trends, industry dynamics, and regulatory landscapes.

3. Continuously Updated Datasets

   • Real-time updates capture changes in professional roles, company structures, and market conditions.
   • Stay ahead of industry shifts and capitalize on emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 170M+ Verified Professional Profiles: Access detailed profiles of European financial professionals across industries and sectors.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for precise targeting.
• Firmographic Data: Understand company sizes, revenue ranges, and geographic footprints to inform your outreach strategy.
• Leadership Insights: Connect with CFOs, financial controllers, and investment managers driving financial strategies.

Key Features of the Dataset:

1. Comprehensive Financial Professional Profiles

   • Identify and connect with key players in finance, including financial analysts, accountants, and consultants.
   • Target professionals responsible for budgeting, investment strategies, regulatory compliance, and financial planning.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (banking, fintech, asset management), geographic location, or job function.
   • Tailor campaigns to align with specific financial needs, such as software solutions, advisory services, or compliance tools.

3. Regional and Industry Insights

   • Leverage data on European financial trends, regulatory challenges, and market opportunities.
   • Refine your approach to align with industry-specific demands and geographic preferences.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Lead Generation

   • Design targeted campaigns to promote financial software, advisory services, or compliance solutions to European financial professionals.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media.

2. Partnership Development and Collaboration

   • Build relationships with financial firms, fintech companies, and investment organizations exploring strategic partnerships.
   • Foster collaborations that enhance financial efficiency, innovation, or regulatory compliance.

3. Market Research and Competitive Analysis

   • Analyze financial trends across Europe to refine product offerings, marketing strategies, and business expansion plans.
   • Benchmark against competitors to identify growth opportunities and emerging demands.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for financial roles, from analysts to CFOs.
   • Provide workforce optimization platforms or training solutions tailored to the financial sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality financial data at competitive prices, ensuring strong ROI for your marketing, sales, and partnership initiatives.

2. Seamless Integration

   • Integrate verified financial data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and boost conversion rates in financial ca...