At the end of February 2025, the DAX index reached 22,551.43 points, marking its highest level since January 2015. Moreover, this also reflected a strong recovery from the global coronavirus (COVID-19) pandemic, having risen from 9,935.84 points at the end of March 2020 and surpassing its pre-pandemic level of approximately 13,249.01 points at the end of December 2019. Origin and composition of the DAX Index The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 40 largest companies by market capitalization listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The count among their number some of the most recognizable companies in the world, such as carmakers Volkswagen and Daimler, sportswear brand adidas, and industrial giants Siemens and BASF. After the DAX, the 50 next-largest German companies are included in the midcap MDAX index, while the 70 next-largest small and medium-sized German companies (ranked from 91 to 160) are included in the SDAX index. The Frankfurt Stock Exchange All the companies included in the DAX family of indices are traded on the Frankfurt Stock Exchange. Dating back to 1585, the Frankfurt Stock Exchange is considered to be the oldest exchange in the world. It is the twelfth largest stock exchange in the world in terms of market capitalization, and accounts for around 90 percent of all equity trading in Germany. Two main trading venues comprise the Frankfurt Stock Exchange: the Börse Frankfurt is a traditional trading floor; while the Xetra is an electronic trading system which accounts for the vast majority of trading volume on Frankfurt Stock Exchange. As of December 2023, the total market capitalization of all companies listed on the Frankfurt Stock Exchange was around two trillion euros.

Germany's main stock market index, the DE40, rose to 23625 points on June 24, 2025, gaining 1.53% from the previous session. Over the past month, the index has declined 1.68%, though it remains 29.97% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on June of 2025.

The value of shares traded on the Frankfurt Stock Exchange jumped in March 2020, with the 160 largest German companies generating a turnover of almost 214 billion euros in that month alone. The vast majority of this turnover - over 80 percent - related to the 30 largest German companies included in the DAX index (as it was at that time), with around 17 percent relating the the MDAX index, and only three percent the SDAX. By June 2024, the monthly turnover value had stabilized at around 79.3 billion euros.

As of March 2025, the software company SAP had the highest market capitalization out of all the DAX companies, with around 2.92 billion euros. The company with the second-highest market capitalization was Siemens, with a market capitalization value of around 1.77 billion euros. Market capitalization reflects the current stock market value of a company and is calculated by multiplying the share price by the number of shares issued. Market capitalization therefore also corresponds to the price that a buyer would have to pay for all of a company's shares in circulation - i.e. a complete takeover.

The DAX is a stock market index composed of the 40 major German blue chip companies trading on the Frankfurt Stock Exchange. At the close of 2024, the DAX (Deutscher Aktienindex) closed at 19,909.04 points. This was the highest closing value of the observed period.What is the DAX index? The DAX is the most important stock index in Germany. It was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The DAX index is comprised of 40 largest and most liquid German companies such as Deutsche Bank, Allianz or Bayer. These companies are traded on the Frankfurt Stock Exchange, which is the oldest exchange worldwide. The index can be viewed as a snapshot of the investment climate in Germany. What is not included in the DAX? Most notably, the DAX, like most indices, is not adjusted for inflation. While inflation has been relatively low in recent years, it might be useful to adjust the historic figures on the index when comparing historic data to current levels. This is particularly important for years when the index appears to increase by a few percentage points, because inflation may have increased at a more rapid rate than the stock prices.

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.

DAX Index: Germany's Economic Pulse?

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 Germany Stock Market Index (DE40) including live quotes, historical charts and news. Germany Stock Market Index (DE40) was last updated by Trading Economics this June 23 of 2025.

The statistic compares the average monthly performance of the DAX index in 2023 compared to 1959. In Germany, the DAX is the most important share index, showing the value trends of the 40 largest and most highly liquid companies listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The average historical performance of the DAX index in September since 1959 amounted to -1.82 percent.

Germany XETRA Frankfurt: Index: DAX data was reported at 8,584.910 NA in Apr 2025. This records an increase from the previous number of 8,489.820 NA for Mar 2025. Germany XETRA Frankfurt: Index: DAX data is updated monthly, averaging 5,890.235 NA from Jan 2016 (Median) to Apr 2025, with 112 observations. The data reached an all-time high of 8,638.520 NA in Feb 2025 and a record low of 4,416.970 NA in Mar 2020. Germany XETRA Frankfurt: Index: DAX 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: XETRA Frankfurt: Monthly.

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.

DAX Index: Barometer of German 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.

DAX Index: Is Germany's Economy on the Rise?

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 total market capitalization of German companies listed on the Frankfurt Stock exchange reached 2.21 trillion euros at the end of 2021. This is above the values found at the end of 2019 and 2017, indicating that Germany's stock market has largely recovered from the financial crash precipitated by the global coronavirus (COVID-19) pandemic in 2020. At the end of 2023, the total market capitalization of German companies listed on the Frankfurt Stock exchange closed at 1.97 trillion euros, a significant decrease compared to the previous year. What is the Frankfurt Stock Exchange? While there are seven stock exchanges in Germany, the Frankfurt Stock Exchange is by far the most important, accounting for around 90 percent of transactions. Run by Deutsche Börse AG, the Frankfurt Stock Exchange is comprised of two exchange trading venues: the traditional trading floor of the Börse Frankfurt, and the electronic trading platform Xetra (which in turn is divided into domestic and international markets). Xetra counts for the vast majority of the trading volume of the Frankfurt Stock Exchange. As an electronic platform, the technology behind Xetra is used by other stock exchanges around the world, strengthening the Frankfurt Stock Exchange’s competitive position while facilitating its capacity to handle international trading. As a result, the Frankfurt Stock Exchange is one of the largest stock exchanges in the world, sitting just outside the global top 10. The DAX Index The most important indicator of the German share market is the DAX index, which is comprised of the 30 largest German companies trading on the Frankfurt Stock Exchange. Some of the more famous companies included in the index are: car manufactures like Volkswagen, BMW and Daimler; clothing and shoe manufacturer Adidas; industrial companies BASF and Siemens; and pharmaceutical company Bayer. Following the DAX is the MDAX index, which covers the 60 next-largest German companies by market cap, then the SDAX index, comprised of the 70 next-largest companies after the MDAX.

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.

DAX Index: German Economic Barometer?

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 weekly value of the DAX index amounted to 23,081.03 as of March 5, 2025. This is well above the value of 13,681.19 points recorded in the middle of February 2020, prior to the global coronavirus (COVID-19) pandemic. All global stock markets were hit by the growing panic about the coronavirus pandemic, which accounts for this drop. The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 30 largest and most liquid companies listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959.

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.

DAX Index: A Mirror to German 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

Germany XETRA Frankfurt: Index: Net Return: DAX data was reported at 1,964.250 NA in Apr 2025. This records an increase from the previous number of 1,935.820 NA for Mar 2025. Germany XETRA Frankfurt: Index: Net Return: DAX data is updated monthly, averaging 1,200.115 NA from Nov 2016 (Median) to Apr 2025, with 102 observations. The data reached an all-time high of 1,969.700 NA in Feb 2025 and a record low of 899.750 NA in Mar 2020. Germany XETRA Frankfurt: Index: Net Return: DAX 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: XETRA Frankfurt: Monthly.

Germany XETRA Frankfurt: Index: Total Return: DAX data was reported at 22,496.980 NA in Apr 2025. This records an increase from the previous number of 22,163.490 NA for Mar 2025. Germany XETRA Frankfurt: Index: Total Return: DAX data is updated monthly, averaging 13,002.975 NA from Jan 2016 (Median) to Apr 2025, with 112 observations. The data reached an all-time high of 22,551.430 NA in Feb 2025 and a record low of 9,495.400 NA in Feb 2016. Germany XETRA Frankfurt: Index: Total Return: DAX 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: XETRA Frankfurt: Monthly.

GDP Nowcast: swda: YoY: Contribution: Equity Market: Germany: Index: DAX data was reported at 0.921 % in 12 May 2025. This records an increase from the previous number of 0.710 % for 05 May 2025. GDP Nowcast: swda: YoY: Contribution: Equity Market: Germany: Index: DAX data is updated weekly, averaging 0.000 % from Jan 2019 (Median) to 12 May 2025, with 332 observations. The data reached an all-time high of 2.782 % in 28 Apr 2025 and a record low of 0.000 % in 07 Apr 2025. GDP Nowcast: swda: YoY: Contribution: Equity Market: Germany: Index: DAX data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Germany – Table DE.CEIC.NC: CEIC Nowcast: Gross Domestic Product (GDP).

In 2024, around **** percent of the management of DAX companies was made up of women. This was an increase of around *** percent compared to the previous year, and it was the highest figure to date.

The biggest revenue out of the DAX companies in 2022 was recorded at Volkswagen, which had a revenue of around 279.2 billion euros. In second place was the Mercedes-Benz-Group with a revenue of 150 billion euros. The DAX is a stock market index made up of 40 major German companies that are trading on the Frankfurt Stock Exchange.