Graph and download economic data for Dividend Yield of Common Stocks on the New York Stock Exchange, Composite Index for United States (M1346BUSM156NNBR) from Jan 1926 to Feb 1969 about dividends, composite, stock market, NY, yield, interest rate, interest, rate, indexes, and USA.

Prices for United States Stock Market Index (US1000) including live quotes, historical charts and news. United States Stock Market Index (US1000) was last updated by Trading Economics this July 14 of 2025.

China Index: Shanghai Stock Exchange: Dividend data was reported at 3,113.740 31Dec2004=1000 in 14 May 2025. This records an increase from the previous number of 3,087.080 31Dec2004=1000 for 13 May 2025. China Index: Shanghai Stock Exchange: Dividend data is updated daily, averaging 2,635.300 31Dec2004=1000 from Apr 2005 (Median) to 14 May 2025, with 4882 observations. The data reached an all-time high of 4,144.660 31Dec2004=1000 in 12 Jun 2015 and a record low of 1,574.930 31Dec2004=1000 in 10 Mar 2014. China Index: Shanghai Stock Exchange: Dividend data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under High Frequency Database’s Financial and Futures Market – Table CN.ZA: Shanghai Stock Exchange: Indices: Daily.

Hong Kong Dividend Yield: Hang Seng Index: by HSCIS: Financials data was reported at 4.460 % pa in Oct 2018. This records an increase from the previous number of 4.070 % pa for Sep 2018. Hong Kong Dividend Yield: Hang Seng Index: by HSCIS: Financials data is updated monthly, averaging 3.920 % pa from Jan 2007 (Median) to Oct 2018, with 142 observations. The data reached an all-time high of 6.990 % pa in Feb 2009 and a record low of 2.240 % pa in Jan 2007. Hong Kong Dividend Yield: Hang Seng Index: by HSCIS: Financials data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited. The data is categorized under Global Database’s Hong Kong SAR – Table HK.Z004: Main Board: Dividend Yield.

Historical dividend payout and yield for Invesco DB Commodity Index Tracking ETF (DBC) since 2008. The current TTM dividend payout for Invesco DB Commodity Index Tracking ETF (DBC) as of July 09, 2025 is $1.12. The current dividend yield for Invesco DB Commodity Index Tracking ETF as of July 09, 2025 is 5.22%.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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.

Can Cavendish Financial (CAV) Chart a Course for 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

Prices for US Tech Composite Index including live quotes, historical charts and news. US Tech Composite Index was last updated by Trading Economics this July 14 of 2025.

Philippines Dividend Yield Ratio: Index Level: PSEi data was reported at 3.445 % in Feb 2025. This records a decrease from the previous number of 3.535 % for Jan 2025. Philippines Dividend Yield Ratio: Index Level: PSEi data is updated monthly, averaging 2.225 % from Jul 2006 (Median) to Feb 2025, with 224 observations. The data reached an all-time high of 6.080 % in Oct 2008 and a record low of 1.529 % in Jan 2018. Philippines Dividend Yield Ratio: Index Level: PSEi data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z004: Philippine Stock Exchange: PE Ratio, PB Ratio and Yield. [COVID-19-IMPACT]

SSE reported GBP3.82 in Dividend Yield for its fiscal semester ending in December of 2024. Data for SSE - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Prices for United States Stock Market Index (USVIX) including live quotes, historical charts and news. United States Stock Market Index (USVIX) was last updated by Trading Economics this July 14 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.

Chart Industries (GTLS) Stock Forecast: Positive Outlook

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.

Chart's (GTLS) Stock Expected to See Moderate Growth, Analysts Say

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.

Evolent Health (EVH) Charts a New Course: Will Growth Sustain the Momentum?

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 Dividend Yield: Actuaries Share Index: FTSE 350 Lower Yield data was reported at 2.380 % pa in Oct 2018. This records an increase from the previous number of 2.170 % pa for Sep 2018. United Kingdom Dividend Yield: Actuaries Share Index: FTSE 350 Lower Yield data is updated monthly, averaging 2.050 % pa from Jan 1999 (Median) to Oct 2018, with 238 observations. The data reached an all-time high of 3.600 % pa in Feb 2009 and a record low of 0.960 % pa in Aug 2000. United Kingdom Dividend Yield: Actuaries Share Index: FTSE 350 Lower Yield data remains active status in CEIC and is reported by Financial Times. The data is categorized under Global Database’s United Kingdom – Table UK.Z002: Financial Times Stock Exchange: Dividend Yield.

Prices for Netherlands Stock Market Index (NL25) including live quotes, historical charts and news. Netherlands Stock Market Index (NL25) was last updated by Trading Economics this July 13 of 2025.

Prices for Slovak Share Index including live quotes, historical charts and news. Slovak Share Index was last updated by Trading Economics this July 14 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.

Aon's (AON) Global Reach: Insurance Giant Charts a Course for 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

Prices for US Bank Index including live quotes, historical charts and news. US Bank Index was last updated by Trading Economics this July 13 of 2025.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this July 14 of 2025.