Germany's main stock market index, the DE40, rose to 23722 points on December 2, 2025, gaining 0.56% from the previous session. Over the past month, the index has declined 1.70%, though it remains 18.51% 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 December of 2025.

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 December 2 of 2025.

As of March 2025, the software company SAP had the highest market capitalization out of all the DAX companies, with around **** billion euros. The company with the second-highest market capitalization was Siemens, with a market capitalization value of around **** 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 total market capitalization of German companies listed on the Frankfurt Stock exchange reached **** 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 **** 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 ** 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 data set contains data on the stock index DAX in Germany – the Deutscher Aktien Index or the GER40. It represents 40 of the largest and most liquid German companies that trade on the Frankfurt Exchange.

The data is publicly available from many economics and financial news sites such as Trading View and Trading Economics.

This particular dataset records the Open, High, Low, Close, Adj Close and Volume from September 2022 to September 2023.

At the end of February 2025, the DAX index reached ********* 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 ******** points at the end of March 2020 and surpassing its pre-pandemic level of approximately ********* 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 ** 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 *** trillion euros.

Graph and download economic data for Index of Stock Prices for Germany (M1123ADEM324NNBR) from Jan 1870 to Dec 1913 about stock market, Germany, and indexes.

The DAX is a stock market index composed of the ** major German blue chip companies trading on the Frankfurt Stock Exchange. At the close of 2024, the DAX (Deutscher Aktienindex) closed at ********* 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 ** 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

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.

Historical dataset of the Germany Stock Market Index (DAX), covering values from 1988-01-01 to 2025-12-02, with the latest releases and long-term trends. Available for free download in CSV format.

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

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

The aim of this investigation is, to describe the development of the German Stock Market during the inter-war period. Causes for the so called change of the stock exchange functions are analysed. The author wants to make a contribution on special aspects of the economic history of the Weimar Republic and the following NS-regime. In his investigation the researcher analyses the activities of the involved players in a historical-institutional framework. The Study’s subjectIn the year 1890 the constitution of security exchange markets and stock markets has been the object of political debate and there has been discussed similar questions according to this topic in public and in policy as today. A current question is about the possibilities to boost the functionality of the security exchange and stock markets, not least in the face of Germany’s position in the global economy. In 1896 as a result of massive political conflicts a stock exchange act has arisen that disappointed the representatives of liberal trading interests because of the restriction of the stock market system’s autonomy and the prohibition of certain forms of trade. In 1908 an amendment to the stock exchange act has been adopted by the parliament. The stock market act in this new form has had validity until today. After the years of the hyperinflation deep changes of the stock market processes has been taken place. This changes can be described as a change of function. The economic-historical study at hand deals with the description of the development of the German security exchange markets during the interwar period. Reasons of the functional changes, which means mainly the decrease in importance, are analysed. In this context the primary investigator’s analysis contributes also to specific aspects of the economic history of the Weimar Republic and the Nazi empire. Due to a lack of date the needed statistical information concerning the period of interest is not available and therefore a statistical analysis cannot meet cliometric requirements. Therefore, the study’s concept is primary a desciptive one. On the basis of the quantitative information an identification of the functional change and the definition of stages of this process is made. The researcher tries to carve out the factors which have led to the functional change particularly during the period between 1924 and 1939. In this context the annual reports of banks, reports of the Chamber of Commerce and Industry, contributions of professional journals, and documents of authorities charged with the stock exchange market, are the empirical basis for the investigation. The researcher analyzed the effects of the banking sector’s concentration-process on the stock exchange market and assessed quantitatively the functional change. On the basis of the collected time series for the period of the late 19th century until 1939 the investigator analyzed the activities at the stock markets. First, the focus on interest is on the development of investments and securities issues. Then information on the securities turnover of German capital market before 1940 are given on the basis of an estimation procedure, developed by the researcher. The sepcial conditions during the inflation between 1914 and 1923 are discussed separately and the long term effects of this hyper-inflation on the stock exchange are identified. The effects of the taxation of stock exchange market visits and the high transaction costs are discussed, too. Used sources for the investigation have been:Archives of German Public Authorities:- finance ministry of the German Reich,- imperial chancellery- Reich´s ministry of economics- reference files of the German Reichsbank- Imperial commissioner of the stock market in Berlin Official Statistics, statistics of trade associations, chambers of commerce, enterprises, the press, and scientific publications. Finally, the author made estimates and calculations. The Study’s data:Data tables are accessible via the search- and download-system HISTAT unter the Topic ‘State: Finances and Taxes’ (= Staat: Finanzen und Steuern). The Study’s data are diveded into the following parts: A. Quantitative Indicators on the Change of Functions (Quantitative Indikatoren des Funktionswandels) A.1 Structure of floatation (Struktur der Wertpapieremission ausgewählter Zeitspannen (1901-1939).)A.2 Tax revenues of exchange turnover (Börsenumsatzsteueraufkommen (1885-1939).)A.3 Vergleich des unkorrigierten mit einem fiktiv möglichen Börsenumsatzsteueraufkommen (1906-1913).A.4 Estimation of everage tax rates (Geschätzte Durchschnittssteuersätze (1884-1913).)A.5 Amount of stock companies of the German Empire (Zahl der Aktiengesellschaften im Deutschen Reich zu bestimmten Jahren (1886-1939).)A.6 Shares listed on the Berlin stock exchange at the end of the year (Die zum Jahresende an der Berliner Börse notierten Aktien (1926-1939).)A.7 Reports und Lombards der Berliner Großbanken in ...

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 ** 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 ***** percent.

DAX 30 Index - Historical chart and current data through 2025.

Germany XETRA Frankfurt: Index: DAX data was reported at 8,930.260 NA in Nov 2025. This records a decrease from the previous number of 8,975.780 NA for Oct 2025. Germany XETRA Frankfurt: Index: DAX data is updated monthly, averaging 5,935.220 NA from Jan 2016 (Median) to Nov 2025, with 119 observations. The data reached an all-time high of 9,015.930 NA in Jul 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.

Germany: Stock market capitalization, billion USD: The latest value from 2024 is 2044.25 billion U.S. dollars, a decline from 2178.05 billion U.S. dollars in 2023. In comparison, the world average is 1647.20 billion U.S. dollars, based on data from 68 countries. Historically, the average for Germany from 1975 to 2024 is 988.57 billion U.S. dollars. The minimum value, 51.4 billion U.S. dollars, was reached in 1975 while the maximum of 2503.05 billion U.S. dollars was recorded in 2021.

Germany Stock Market Expectation: Japan data was reported at 37.500 % in Mar 2021. This records a decrease from the previous number of 37.800 % for Feb 2021. Germany Stock Market Expectation: Japan data is updated monthly, averaging 34.600 % from Dec 1991 (Median) to Mar 2021, with 352 observations. The data reached an all-time high of 74.600 % in Dec 1999 and a record low of -8.200 % in Jun 2020. Germany Stock Market Expectation: Japan data remains active status in CEIC and is reported by Leibniz Centre for European Economic Research. The data is categorized under Global Database’s Germany – Table DE.S001: Indicator of Economic Sentiment: ZEW.

Key information about Germany Market Capitalization

• Germany Market Capitalization accounted for 2,757.016 USD bn in Oct 2025, compared with a percentage of 2,801.729 USD bn in the previous month
• Germany Market Capitalization is updated monthly, available from Jan 1998 to Oct 2025
• The data reached an all-time high of 2,851.942 USD bn in Jun 2025 and a record low of 627.521 USD bn in Mar 2003

CEIC converts monthly Market Capitalization into USD. Deutsche Börse Group provides Market Capitalization in EUR. The Federal Reserve Board period end market exchange rate is used for currency conversions.