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 September 5 of 2025.

Euro Area's main stock market index, the EU50, rose to 5346 points on September 4, 2025, gaining 0.43% from the previous session. Over the past month, the index has climbed 1.83% and is up 11.02% 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 September of 2025.

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

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.

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 (Euronext 100) including live quotes, historical charts and news. Euro Area Stock Market Index (Euronext 100) was last updated by Trading Economics this September 7 of 2025.

Graph and download economic data for Share Prices: All Shares/Broad: Total for Euro Area (19 Countries) (SPASTT01EZM657N) from Jan 1987 to Jun 2025 about stock market, Euro Area, and Europe.

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

European Metals (EMH) Stock: A Rare Earth Play for the Future

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

In 2025, stock markets in the United States accounted for roughly ** percent of world stocks. The next largest country by stock market share was China, followed by the European Union as a whole. The New York Stock Exchange (NYSE) and the NASDAQ are the largest stock exchange operators worldwide. What is a stock exchange? The first modern publicly traded company was the Dutch East Industry Company, which sold shares to the general public to fund expeditions to Asia. Since then, groups of companies have formed exchanges in which brokers and dealers can come together and make transactions in one space. Stock market indices group companies trading on a given exchange, giving an idea of how they evolve in real time. Appeal of stock ownership Over half of adults in the United States are investing money in the stock market. Stocks are an attractive investment because the possible return is higher than offered by other financial instruments.

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

Brain Machine Learning Stock Rankings are generated daily and based on the predicted future returns of a dynamic universe of the largest 1,000 US stocks across four time horizons: 2,3, 5, 10 and 21 days. The universe is updated yearly.

Model inputs include stock specific features such as fundamentals and price-volume related metrics, market data such as volatility and other financial stress indicators, and calendar related signals such as day or month anomalies.

Data Dictionary https://braincompany.co/assets/files/bsr_data_dictionary.json

Factsheet https://braincompany.co/assets/files/BSR_summary.pdf

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

The price of Deutsche Bank share on the Frankfurt Stock Exchange amounted to ***** euros at the end of 2023. This was an increase compared to the previous year. Deutsche Bank's share price was the highest in 2006, at ***** euros, before the Financial Crisis. The lowest year close was observed in 2019, at **** euros.
Deutsche Bank finances Deutsche Bank has closed many of its branches worldwide. This signals that it is trying to decrease its costs. Examining its quarterly net income or loss suggests that the bank faced a difficult business climate in the last several years, although it managed to increase its net income in 2022. The period from 2018 to 2020 was a particularly hard period for the shareholders as well, since the bank was unable to pay dividends. More on the European banking industry Deutsche Bank is among the leading banks in Europe by assets. While struggling to recover from the Financial Crisis of 2008, the continent was hit by the Eurozone Crisis, spurned by high national debt in several countries in southern Europe. Deutsche Bank and other European banks were, and remain, exposed to investments that were less secure than expected.

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

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

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 dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

This dataset provides values for STOCK PRICE VOLATILITY WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.