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.

Euro Area's main stock market index, the EU50, rose to 5329 points on July 23, 2025, gaining 0.75% from the previous session. Over the past month, the index has climbed 0.60% and is up 9.61% 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.

Prices for Euro Stoxx 50 Volatility EUR Price Index including live quotes, historical charts and news. Euro Stoxx 50 Volatility EUR Price Index was last updated by Trading Economics this July 23 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.

Euro Stoxx 50: Back on Track?

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.

Euro Stoxx 50 Index: A Barometer of European Economic Health?

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.

Euro Stoxx 50 Index Stock Price 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

Prices for Euro Area Stock Market Index (EU50) including live quotes, historical charts and news. Euro Area Stock Market Index (EU50) was last updated by Trading Economics this July 24 of 2025.

Germany Frankfurt Stock Exchange: Index: Euro Stoxx 50 data was reported at 5,160.220 NA in Apr 2025. This records a decrease from the previous number of 5,248.390 NA for Mar 2025. Germany Frankfurt Stock Exchange: Index: Euro Stoxx 50 data is updated monthly, averaging 3,728.285 NA from Sep 2017 (Median) to Apr 2025, with 92 observations. The data reached an all-time high of 5,463.540 NA in Feb 2025 and a record low of 2,786.900 NA in Mar 2020. Germany Frankfurt Stock Exchange: Index: Euro Stoxx 50 data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Germany – Table DE.EDI.SE: Frankfurt Stock Exchange: Euro Stoxx: Monthly.

The three pairs of indices out of S&P 500, FTSE 100 and EURO STOXX 50, and the different currency terms used.

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.

Will the Euro Stoxx 50 Index Reach New Heights?

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 observed periods of cointegration and Granger causality (in long run) between the S&P 500 and EURO STOXX 50, during 1998–2015.

The observed periods of cointegration and Granger causality (in long run) between the FTSE 100 and EURO STOXX 50 during 1998–2015.

Prices for Euro Stoxx Banks including live quotes, historical charts and news. Euro Stoxx Banks was last updated by Trading Economics this July 24 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.

Euro Stoxx 50 index to see moderate gains this year.

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

Kurssturz durch Corona - das sich zu Beginn des Jahres 2020 weltweit ausbreitende Corona-Virus hatte auch die Börsen fest im Griff. So kam es insbesondere im März des Jahres zu immensen Kursverlusten des EURO STOXX 50. Den größten Kurseinbruch verzeichnete der Index am 12. März. Während der EURO STOXX 50 den Handelstag noch bei einem Stand von 2.905,56 Punkten eröffnete, beendete er ihn bei 2.545,23 Punkten - mit 12,4 Prozent einer der größten Kursverluste innerhalb eines Tages in der Geschichte des EURO STOXX 50. In den folgenden Wochen stabilisierte sich der Index jedoch und kletterte bis Ende Mai wieder auf über 3.000 Punkte - annähernd das Niveau, das er vor der Corona-Krise hatte.

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.

Euro Stoxx 50 Index: Navigating the European Economic Landscape?

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

Context

COVID-19 or Corona Virus is on anyone's lips, since it has affected (and still affecting) a lot of aspects in our lives. From when the virus was first considered a pandemic until now, it has driven the markets crazy, having one of the most significant effects on the past years. No one was able to predict this and none of the financial models was prepared for the huge change the market has suffered. This dataset aims to explain the market evolution before and after the COVID-19

Content

Financial historical data from the World Major Indices, including: Shanghai, FTSE MIB, S&P 500, Nasdaq, Dow 30, Euro Stoxx 50, and much more. The dataset contains: OHLC values, the Volume and the Currency.

Note that the dataset has been generated using investpy an open-source Python package to extract financial data from Investing.com, and you can find all the usage information and documentation at: https://github.com/alvarobartt/investpy.

Inspiration

This dataset aims to explain the market evolution before and after the COVID-19 so as to extract conclusions based on just market data or maybe aggregating external data such as news reports, tweets, etc. so feel free to use this dataset and combine it with others so that we, the community, can develop useful kernels so as to analyse and understand this situation and its impacts. So it is also an open call to researchers, data scientists, financial analysts, etc. so to collaborate together in a market study on the impacts of COVID-19.

Acknowledgements

This dataset been created by Álvaro Bartolomé del Canto using investpy so as to retrieve the historical data from Investing.com. Also, the banner image is property of Investing.com since it is an Investing.com Weekly Comic.

Statistical analysis of dynamic error correction coefficients (absolute value) of ECTs.

We investigate high-frequency reactions in the Eurozone stock market and the UK stock market during the time period surrounding the European Central Bank (ECB) and the Bank of England (BoE)'s interest rate decisions assessing how these two markets react and co-move influencing each other.

The effects are quantified by measuring linear and non-linear transfer entropy combined with a Bivariate Empirical Mode Decomposition (BEMD) from a dataset of 1-minute prices for the Euro Stoxx 50 and the FTSE 100 stock indices.

We uncover that central banks' interest rate decisions induce an upsurge in intraday volatility that is more pronounced on ECB announcement days and there is a significant information flow between the markets with prevalent direction going from the market where the announcement is made towards the other.

Five small files contain daily index data for volatility and implied volatility of EURO STOXX 50, EURO STOXX 600 (only volatility data) and FTSE 100. This was collected from Refinitiv EIKON.The large file contains ESG ratings of all European companies, and the adjusted daily prices of most of these companies. The ESG ratings were collected from Refinitiv EIKON. The adjusted daily prices were collected from Yahoo Finance!.