Graph and download economic data for ICE BofA CCC & Lower US High Yield Index Effective Yield (BAMLH0A3HYCEY) from 1996-12-31 to 2025-07-30 about CCC, yield, interest rate, interest, rate, and USA.

Graph and download economic data for ICE BofA CCC & Lower US High Yield Index Option-Adjusted Spread (BAMLH0A3HYC) from 1996-12-31 to 2025-07-31 about CCC, option-adjusted spread, yield, interest rate, interest, rate, and USA.

Graph and download economic data for ICE BofA CCC & Lower US High Yield Index Semi-Annual Yield to Worst (BAMLH0A3HYCSYTW) from 1996-12-31 to 2025-07-31 about CCC, YTW, yield, interest rate, interest, rate, and USA.

Corp Bond: ADTV: HY: CCC: >= 1,000,000 and < 5,000,000 data was reported at 635.731 USD mn in Mar 2025. This records an increase from the previous number of 584.003 USD mn for Dec 2024. Corp Bond: ADTV: HY: CCC: >= 1,000,000 and < 5,000,000 data is updated quarterly, averaging 640.336 USD mn from Jun 2019 (Median) to Mar 2025, with 24 observations. The data reached an all-time high of 1.026 USD bn in Mar 2020 and a record low of 525.985 USD mn in Sep 2023. Corp Bond: ADTV: HY: CCC: >= 1,000,000 and < 5,000,000 data remains active status in CEIC and is reported by Financial Industry Regulatory Authority, Inc.. The data is categorized under Global Database’s United States – Table US.Z: US Corporate Bond Average Daily Trading Volume: High Yield.

United States Corp Bond: ADTV: HY: CCC: >= 10,000,000 < 25,000,000 data was reported at 253.244 USD mn in Mar 2025. This records a decrease from the previous number of 261.575 USD mn for Dec 2024. United States Corp Bond: ADTV: HY: CCC: >= 10,000,000 < 25,000,000 data is updated quarterly, averaging 195.893 USD mn from Jun 2019 (Median) to Mar 2025, with 24 observations. The data reached an all-time high of 299.377 USD mn in Mar 2020 and a record low of 136.388 USD mn in Dec 2023. United States Corp Bond: ADTV: HY: CCC: >= 10,000,000 < 25,000,000 data remains active status in CEIC and is reported by Financial Industry Regulatory Authority, Inc.. The data is categorized under Global Database’s United States – Table US.Z: US Corporate Bond Average Daily Trading Volume: High Yield.

United States Corp Bond: ADTV: HY: CCC: >= 5,000,000 < 10,000,000 data was reported at 334.825 USD mn in Mar 2025. This records an increase from the previous number of 323.682 USD mn for Dec 2024. United States Corp Bond: ADTV: HY: CCC: >= 5,000,000 < 10,000,000 data is updated quarterly, averaging 314.675 USD mn from Jun 2019 (Median) to Mar 2025, with 24 observations. The data reached an all-time high of 480.814 USD mn in Mar 2020 and a record low of 224.143 USD mn in Sep 2023. United States Corp Bond: ADTV: HY: CCC: >= 5,000,000 < 10,000,000 data remains active status in CEIC and is reported by Financial Industry Regulatory Authority, Inc.. The data is categorized under Global Database’s United States – Table US.Z: US Corporate Bond Average Daily Trading Volume: High Yield.

Graph and download economic data for ICE BofA Euro High Yield Index Effective Yield (BAMLHE00EHYIEY) from 1997-12-31 to 2025-07-30 about Euro Area, Europe, yield, interest rate, interest, rate, and indexes.

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.

CCC Intelligent Solutions: (CCCS) A Bright Future for Intelligent Solutions?

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 Moody's Seasoned Baa Corporate Bond Yield (DBAA) from 1986-01-02 to 2025-07-30 about Baa, bonds, yield, corporate, interest rate, interest, rate, and USA.

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.

Computacenter (CCC) Poised for Growth in a Tech-Driven World

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

Daily mean values of weather variables at experimental site during each of six months of the maize growing season from 2011 to 2012.