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.

Prices for Euro Area Stock Market Index (EU600) including live quotes, historical charts and news. Euro Area Stock Market Index (EU600) was last updated by Trading Economics this October 16 of 2025.

Euro Area's main stock market index, the EU50, fell to 5608 points on October 17, 2025, losing 0.77% from the previous session. Over the past month, the index has climbed 2.78% and is up 12.47% 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 October of 2025.

The average for 2021 based on 36 countries was 20.67 percent. The highest value was in Greece: 31.83 percent and the lowest value was in Russia: 12.32 percent. The indicator is available from 1984 to 2021. Below is a chart for all countries where data are available.

Graph and download economic data for Volatility of Stock Price Index for Euro Area (DISCONTINUED) (DDSM01EZA066NWDB) from 1984 to 2015 about volatility, stocks, Euro Area, Europe, price index, indexes, and price.

Stock prices of the largest European banks fell sharply in March 2023, as the collapse of Silicon Valley Bank and First Republic in the U.S. crumbled confidence in the sector. Shortly after the second and third largest U.S. bank failures, Credit Suisse went under, which pushed the stock prices of leading European banks down further. Towards the end of the month, stock prices increased notably, but remained well below prices at the start of March.

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 October 18 of 2025.

Graph and download economic data for Financial Market: Share Prices for Euro Area (19 Countries) (SPASTT01EZM661N) from Dec 1986 to Aug 2025 about stock market, Euro Area, and Europe.

European Seeds and Planting Stock Real Price Index for Agriculture by Country, 2022 Discover more data with ReportLinker!

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

B&M European Value stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The dataset contains stock market indices and stock prices of companies.

Graph and download economic data for Financial Market: Share Prices for Euro Area (19 Countries) (SPASTT01EZQ661N) from Q1 1987 to Q2 2025 about stock market, Euro Area, and Europe.

Air France-KLM, one of the largest airliners in Europe, saw its stock prices decrease significantly in early March 2020, falling by over ** percent between February and March, 2020. This was due to an unexpected decrease in oil prices, as announced by Saudi Arabia over a dispute with Russia. Indirectly, this dispute was caused by the outbreak of the coronavirus as this lead to lower consumer demand for fuel (for example, due to a lower inclination to travel by plane). As of January 10, 2023 the Air France-KLM stock price was sitting at **** euros, less than half of the value seen in early 2020.

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.

AEX Index: Will Europe's Top Performers Keep Soaring?

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

Stock Price Time Series for Baring Emerging Europe Plc. Baring Emerging Europe PLC is a closed-ended equity mutual fund launched and managed by Baring Asset Management Limited. The fund invests in the public equity markets of European emerging market countries. It seeks to invest in stocks of companies operating across diversified sectors. It invests in stocks of companies across diversified market capitalizations. The fund primarily invests in reasonably priced growth (GARP) stocks of companies. It employs fundamental analysis with a combination of top-down and bottom-up stock picking approaches, focusing on such factors as growth, liquidity, currency, management, and valuation to create its portfolio. The fund benchmarks the performance of its portfolios against a customized benchmark based on the FTSE Greater Eastern Europe with Turkey Index, within which Russia and Turkey are reduced to 50 per cent. and 15 per cent. of their Index market capitalizations respectively. It was formerly known as Baring Emerging Europe Trust. Baring Emerging Europe PLC was formed in January 1994 and is domiciled in the United Kingdom.

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.

European Elevation: Will EWCZ Stock 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

This line chart displays highest price by date using the aggregation sum. The data is filtered where the stock is EU.V. The data is about stocks per day.

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

US stock prices listed on Standard & Poor’s 500 (S&P 500) Stock Index and UK stock prices from the London Stock Exchange (LSE)