Germany's main stock market index, the DE40, fell to 24304 points on June 6, 2025, losing 0.08% from the previous session. Over the past month, the index has climbed 5.14% and is up 30.97% compared to the same time last year, 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 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.

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 9 of 2025.

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.

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

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

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.

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.

muln german stock market

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 market return (%, year-on-year) in Germany was reported at 23.68 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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 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 subject In 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...

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.

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

Key information about Germany Market Capitalization

• Germany Market Capitalization accounted for 2,182.882 USD bn in Dec 2023, compared with a percentage of 2,100.094 USD bn in the previous month
• Germany Market Capitalization is updated monthly, available from Jan 1998 to Dec 2023
• The data reached an all-time high of 2,641.451 USD bn in May 2021 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.

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 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.

Stock market capitalization to GDP (%) in Germany was reported at 59.38 % in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Stock market capitalization to GDP - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Techsalerator's Corporate Actions Dataset in Germany offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 23800 companies traded on the Berlin Stock Exchange (XBER).

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Top 5 used data fields in the Corporate Actions Dataset for Germany:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Germany:

Mergers and Acquisitions (M&A): Germany has a robust M&A environment with companies engaging in mergers, acquisitions, and strategic partnerships across various industries. These actions can drive consolidation, growth, and market expansion.

IPOs and Capital Raising: Initial Public Offerings (IPOs) and other capital-raising activities are significant corporate actions in Germany. Companies looking to raise funds for expansion or development often tap into the capital markets.

Technology and Innovation: Corporate actions related to technology adoption, digital transformation, and innovation are common in Germany's industrial and technological sectors. Investments in research and development, as well as partnerships with startups, drive these actions.

Sustainability Initiatives: Germany places strong emphasis on sustainability and environmental responsibility. Corporate actions related to green initiatives, renewable energy projects, and sustainable practices are prominent.

Labor and Workforce Relations: Corporate actions involving labor unions, workforce negotiations, and employee engagement are important in Germany's employment landscape. These actions can impact company operations, labor agreements, and overall productivity.

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Top 5 financial instruments with corporate action Data in Germany

DAX Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Frankfurt Stock Exchange. The DAX includes some of the largest and most influential companies in Germany, providing insights into the overall health of the German economy.

DAX Foreign Company Index: The index that tracks the performance of foreign companies listed on the Frankfurt Stock Exchange, if foreign listings were present. This index would offer insights into the performance of international companies operating in Germany.

EuroMart: A Germany-based supermarket chain with operations across various regions. EuroMart focuses on providing a wide range of consumer goods and groceries to customers throughout Germany.

GermanyBank Group: A financial services provider headquartered in Germany, offering a range of banking and financial solutions to individuals and businesses across the country.

AgroTech Germany: A company dedicated to advancing agricultural technology and innovation in Germany. AgroTech develops and distributes advanced farming equipment, digital tools, and solutions to support sustainable agriculture.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Germany, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Germany?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended...

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.

German American Bancorp (GABC) - Riding the Wave of Regional Growth

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