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.

Indeks pasar saham utama Jerman, DE40, naik menjadi 23837 poin pada 28 November 2025, naik 0,29% dari sesi sebelumnya. Selama sebulan terakhir, indeks tersebut turun 1,19%, meskipun tetap 21,45% lebih tinggi dari tahun sebelumnya, menurut perdagangan pada kontrak untuk perbedaan (CFD) yang melacak indeks acuan ini dari Jerman. Nilai saat ini, data historis, perkiraan, statistik, grafik dan kalender ekonomi - Jerman - Pasar Saham.

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 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 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.

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.

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.

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.

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.

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.

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

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 vast majority of stock trades in Frankfurt were made through the electronic Xetra trading venue, rather than the traditional Börse Frankfurt trading floor. The number of prime standard shares traded on the Frankfurt Stock Exchange jumped to over ************* units during **********, due to the economic crash caused coronavirus pandemic. At this time, the DAX index - which is comprised of the ** largest German companies on the Frankfurt Stock Exchange who meet the prime standard - fell by around ** percent. In ********, the monthly number of total transactions amounted to around *** billion.

Index Time Series for iShares Core DAX® UCITS ETF (DE) EUR (Acc). The frequency of the observation is daily. Moving average series are also typically included. iShares DAX® (DE) is an exchange traded fund (ETF) that aims to track the performance of the DAX® Index as closely as possible. The ETF invests in physical index securities. The DAX® Index offers exposure to the 30 largest and most traded stocks listed on the Prime Standard segment of the Frankfurt Stock Exchange. Companies qualify if they are either domiciled in Germany or a minimum of 33% of their stock turnover is traded of the Frankfurt Stock Exchange and they are domiciled in an EU or EFTA country. The index is free float market capitalisation weighted. iShares ETFs are funds managed by BlackRock. They are transparent, cost-efficient, liquid vehicles that trade on stock exchanges like normal securities. iShares ETFs offer flexible and easy access to a wide range of markets and asset classes.

We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a time-varying advertising, public perception or rumor, in favor or against one of two possible trading behaviors, thus breaking the symmetry of the system and acting as a continuously varying exogenous shock. As an illustration, we use a well-known German Indicator of Economic Sentiment as information input and compare our results with Germany’s leading stock market index, the DAX, in order to calibrate some of the model parameters. We study the conditions for the ensemble of agents to more accurately follow the information input signal. The response of the system to the external information is maximal for an intermediate range of values of a market parameter, suggesting the existence of three different market regimes: amplification, precise assimilation and undervaluation of incoming information.

MDAX

The MDAX is a stock index which lists German companies trading on the Frankfurt Stock Exchange. The index is calculated by Deutsche Börse.It includes the 50 Prime Standard shares that rank in size immediately below the companies included in the DAX index. The company size is calculated based on a combination of order book volume and market capitalization. The index is based on prices generated in the electronic trading system Xetra.

Content

This dataset contains daily, weekly and monthly data from the MDAX fro 1996 till mid 2023.

Acknowledgements

Yahoo Finance API development team

hello, this is France, Germany and London stock index daily historical daily data. who can use various historical analysis for investment or show direction of economy.

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 Poised for Cautious Optimism: Economic Winds to Shape the German Index

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