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.

Inflation: Friend or Foe to the 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

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 ...

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.

LON:STG Stock: The Stock Market Bubble Is About to Burst

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

Graph and download economic data for Producer Price Index by Industry: Investment Banking and Securities Intermediation: Dealer Transactions, Debt Securities and All Other Trading (PCU523110523110202) from Dec 1999 to May 2025 about dealers, investment, trade, debt, securities, banks, depository institutions, PPI, industry, inflation, price index, indexes, price, and USA.

United States - Market Yield on U.S. Treasury Securities at 20-Year Constant Maturity, Inflation-Indexed was 2.47% in May of 2025, according to the United States Federal Reserve. Historically, United States - Market Yield on U.S. Treasury Securities at 20-Year Constant Maturity, Inflation-Indexed reached a record high of 3.20 in October of 2008 and a record low of -0.72 in November of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Market Yield on U.S. Treasury Securities at 20-Year Constant Maturity, Inflation-Indexed - last updated from the United States Federal Reserve on May of 2025.

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.

[Video] S&P 500: Bull or Bear?

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

Venezuela's main stock market index, the IBC, rose to 372580 points on June 30, 2025, gaining 0.66% from the previous session. Over the past month, the index has climbed 21.38% and is up 363.09% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Venezuela. Venezuela Stock Market (IBVC) - values, historical data, forecasts and news - updated on July of 2025.

High-frequency trading consists of companies that trade large numbers of orders of financial securities in fractions of a second using quantitative trading algorithms. High-frequency trading is a subset of quantitative investing, which employs algorithms that analyze financial data to conduct trades. This industry is growing due to advancements in technology that have enabled investors to trade at faster rates than ever. Many factors have caused revenue to rise during the current period. During the pandemic investor uncertainty soared and rattled financial markets. As a result, trading volumes climbed leading to greater industry demand and revenue growth as firms capitalized on rapid transactions. The industry has also increasingly invested in computers and software to enhance the speed and efficiency of trade execution. Increased computer and software investments also help the industry improve portfolio optimization, which helps firms maximize gains while reducing market risks. As inflation soared in the latter part of the period, the Federal Reserve raised interest rates. Higher rates made bonds more attractive to investors, reducing investment in the stock market and the industry’s services. This posed a threat to high-frequency traders, although in 2024, the Federal Reserve cut interest rates by half a point as inflationary pressures eased. This will limit investments in bonds and attract investment back into equities. Overall, industry revenue has grown at a CAGR of 5.7% to $7.1 billion over the past five years, including an expected decline of 3.7% in 2024 alone. Over the next five years, steady income growth will raise access to credit, enabling consumers to invest more in the stock market. As competition among financial institutions soars, private investment in computers and software will increase. These investments will make high-frequency trading more efficient, increasing its attractiveness. Investor uncertainty is anticipated to decline, so the volume of trades will be relatively low and the industry won't have a vital source of downstream demand. Overall, industry revenue is expected to lag at a CAGR of 2.7% to $6.2 billion over the five years to 2029.

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.

Inflation: How It's Affecting Your Shopping Habits

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.

Inflation Drives People to Google Negative Concepts

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

United Kingdom's main stock market index, the GB100, fell to 8761 points on June 30, 2025, losing 0.43% from the previous session. Over the past month, the index has declined 0.15%, though it remains 7.28% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on July of 2025.

The Study’s subject: The investigator’s aim is to determine the volume of stock trade. A sample of papers consisting of shares, government’s bond issues, corporate bond issues, bonds of mortgage banks, bonds of so called ‘Landschaftsbanks’, bonds of annuity banks, and floated subscription rights is the focus of the investigation.

With regard to the periods of German history the development of the stock market is described. The periods are: - the influence of the First World War 1914 to 1918 on the stock market - the period of inflation 1919 to 1924 - apparent return of normality 1924 to 1929 - the influence of world economic crisis 1929 to 1933 - the Nazi Socialist economic policy 1933 to 1939 - finally, the Second World War 1939 to 1945.

Important comment on the data: Taxes and the system of taxes have changed over time under investigation. Therefore, the development of stock exchange turnover tax is only one indication among others for the development of securities transactions. Furthermore, it has to be taken into account, that the reported values for the period of inflation cannot be used for comparisons with other periods.

Data-Tables in HISTAT (subject: money and currency, financial sector, in German: Thema: Geld und Währung, Finanzsektor):

A. Volume of Stock Trade in Germany A.1 Development of stock exchange turnover tax in millions of M/RM (1910-1944). A.2 Circulation of securities of domestic issuers in Billions of M/RM (1910-1944).

B. Apparent return of normality after the period of inflation
B.1 monthly averages of share prices (monthly statistics, index: 1924 to 1926 = 100, (1925-1929)). B.2 Monthly bonds prices in percent of the nominal value (monthly statistics, (1925-1929)). B.3 Stock market in Breslau: Firms and brokers authorized for stock trading (1850-1931/32).

C. Influence of economic crisis
C.1 Monthly share prices (monthly statistics, index: 1924 to 1926=100 (1930-1934)). C.2 Monthly bonds prices in percent of the nominal value (monthly statistics, (1930-1934)).

D. Influence of Nazi Socialist economic policy and stock exchange during World War II D.1 Share prices of the company ‚Rütgerswerke-AG’ in Berlin (1933-1937). D.2 Index of share prices, index: 1924 to 1926=100 (1924-1943).

Commodity Index Funds Market Outlook

The global commodity index funds market size was valued at approximately $200 billion in 2023 and is projected to reach nearly $400 billion by 2032, growing at a robust CAGR of 7.5% during the forecast period. The significant growth in this market can be attributed to the increasing demand for diversification in investment portfolios and the inherent benefits of hedging against inflation that commodity investments provide. Furthermore, the volatility in global stock markets and geopolitical uncertainties have led investors to seek safer, more stable investment avenues, thus driving the growth of commodity index funds.

One of the primary growth factors propelling the commodity index funds market is the rising awareness among investors about the advantages of commodity investments as a hedge against inflation. Commodities, unlike stocks and bonds, often move inversely to the stock market, providing a cushion during market downturns. This characteristic makes commodity index funds an attractive option for risk-averse investors and those looking to balance their portfolios. Additionally, the globalization of trade and the increasing demand for raw materials in emerging markets have further spurred the demand for commodity investments.

Technological advancements in trading platforms have also significantly contributed to the growth of this market. The advent of sophisticated online platforms has made it easier for retail investors to access and invest in commodity index funds. These platforms offer a range of tools and resources that help investors make informed decisions, thereby democratizing access to commodity investments. Moreover, the rise of robo-advisors and algorithm-based trading strategies has further simplified the investment process, attracting a new generation of tech-savvy investors.

The regulatory landscape has also played a crucial role in shaping the commodity index funds market. Governments and financial regulatory bodies across the globe have been working to create a transparent and secure trading environment. Regulatory reforms aimed at reducing market manipulation and increasing transparency have instilled confidence among investors, thereby boosting the market. Additionally, tax incentives and favorable policies for commodity investments in various countries have also contributed to market growth.

In terms of regional outlook, North America holds a significant share of the global commodity index funds market, followed by Europe and Asia Pacific. The presence of well-established financial markets and a high level of investor awareness in North America are key factors driving the market in this region. Europe, with its strong regulatory framework and increasing adoption of alternative investment strategies, is also witnessing substantial growth. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by the rapid economic growth in countries like China and India, and the increasing interest in commodity investments among institutional and retail investors.

Fund Type Analysis

When analyzing the market by fund type, Broad Commodity Index Funds dominate the landscape. These funds invest in a diversified portfolio of commodities, making them a popular choice for investors seeking broad exposure to the commodity markets. The broad commodity index funds are designed to track the performance of a basket of commodities, ranging from energy products to metals and agricultural goods. This diversification helps mitigate risks associated with the volatility of individual commodities, thereby providing a more stable investment option for risk-averse investors.

Single Commodity Index Funds, on the other hand, focus on specific commodities such as gold, oil, or agricultural products. These funds appeal to investors who have a strong conviction about the performance of a particular commodity. For instance, during periods of economic uncertainty, gold-focused funds often see a surge in demand as investors flock to the safe-haven asset. Similarly, energy-focused funds attract investors when there are disruptions in oil supply or significant geopolitical events affecting oil prices. While these funds offer the potential for high returns, they also come with higher risks due to their lack of diversification.

Sector Commodity Index Funds are another important segment within the commodity index funds market. These funds concentrate on commodities within a specific sector, such as energy, agriculture, or metals, allowing investors to target particular segments of the commo

Strong returns in various financial markets and increased trading volumes have benefited businesses in the industry. Companies provide underwriting, brokering and market-making services for different financial instruments, including bonds, stocks and derivatives. Businesses benefited from improving macroeconomic conditions despite high inflationary economic environment. However, in 2024, the Fed slashed interest rates as inflationary pressures eased , limiting interest income from fixed-income securities for the industry. The Fed seeks to further cut interest rates but will monitor inflation, employment, the effects of tariffs and other economic factors before making further rate cut decisions. Overall, revenue has been growing at a CAGR of 7.0% over the past five years and is expected to total $456.6 billion in 2025, with revenue expected to decline 0.9% in the same year. In addition, industry profit is expected to climb to 13.0% over the five years to 2025. While many industries struggled at the onset of the period due to economic disruptions due to the pandemic and supply chain issues, businesses benefited from the volatility. Primarily, companies have benefited from increased trading activity on behalf of their clients due to fluctuations in asset prices. This has led to higher trade execution fees for firms at the onset of the period. Similarly, debt underwriting increased as many businesses have turned to investment bankers to help raise cash for various ventures. Also, improved scalability of operations, especially regarding trading services conducted by securities intermediates, has helped increase industry profits. Structural changes have forced the industry's smaller businesses to evolve. Because competing in trading services requires massive investments in technology and compliance, boutique investment banks have alternatively focused on advising in merger and acquisition (M&A) activity. Boutique investment banks' total share of M&A revenue is forecast to grow through the end of 2030. Furthermore, the industry will benefit from improved macroeconomic conditions as inflationary pressures are expected to ease. This will help asset values rise and interest rate levels to be cut, thus allowing operators to generate more from equity underwriting and lending activities. Overall, revenue is forecast to grow at a CAGR of 2.2% to $507.9 billion over the five years to 2030.

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.

S&P 500 Inflation Risk

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

Brazil's main stock market index, the IBOVESPA, rose to 138855 points on June 30, 2025, gaining 1.45% from the previous session. Over the past month, the index has climbed 1.51% and is up 11.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Brazil. Brazil Stock Market (BOVESPA) - values, historical data, forecasts and news - updated on July of 2025.

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.

Inflation on the Rise: What Does This Mean for You?

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

Argentina's main stock market index, the Merval, fell to 1994825 points on June 30, 2025, losing 2.27% from the previous session. Over the past month, the index has declined 9.58%, though it remains 25.09% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Argentina. Argentina Stock Market (MERVAL) - values, historical data, forecasts and news - updated on July of 2025.

United States - Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Inflation-Indexed was 2.62% in May of 2025, according to the United States Federal Reserve. Historically, United States - Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Inflation-Indexed reached a record high of 2.62 in May of 2025 and a record low of -0.44 in November of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Inflation-Indexed - last updated from the United States Federal Reserve on June of 2025.

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.

Does S&P 500 beat inflation?

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