The yield on US 30 Year Bond Yield rose to 4.87% on July 3, 2025, marking a 0.05 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.02 points, though it remains 0.33 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 30 Year Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on US 10 Year Note Bond Yield rose to 4.35% on July 3, 2025, marking a 0.07 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.01 points and is 0.02 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. US 10 Year Treasury Bond Note Yield - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States (IRLTLT01USM156N) from Apr 1953 to May 2025 about long-term, 10-year, bonds, yield, government, interest rate, interest, rate, and USA.

The yield on Japan 10Y Bond Yield eased to 1.44% on July 4, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.03 points, though it remains 0.37 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Japan 10 Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on China 10Y Bond Yield rose to 1.64% on July 4, 2025, marking a 0 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.04 points and is 0.62 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. China 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

China Bond Yield: Treasury Bond: 10 Year data was reported at 1.679 % pa in 16 May 2025. This records a decrease from the previous number of 1.680 % pa for 15 May 2025. China Bond Yield: Treasury Bond: 10 Year data is updated daily, averaging 3.254 % pa from Mar 2006 (Median) to 16 May 2025, with 4806 observations. The data reached an all-time high of 4.722 % pa in 20 Nov 2013 and a record low of 1.596 % pa in 06 Feb 2025. China Bond Yield: Treasury Bond: 10 Year data remains active status in CEIC and is reported by China Central Depository & Clearing Co., Ltd. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MF: PBC & CCDC: Treasury Bond and Other Bond Yield: Daily.

The yield on Canada 10Y Bond Yield eased to 3.36% on July 4, 2025, marking a 0.02 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.11 points, though it remains 0.14 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Canada 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

Government Bond Yield: Daily: 10 Years: Maturity 2033: Sale Price Morning data was reported at 866.300 BRL in 12 May 2025. This records an increase from the previous number of 865.460 BRL for 09 May 2025. Government Bond Yield: Daily: 10 Years: Maturity 2033: Sale Price Morning data is updated daily, averaging 907.785 BRL from Feb 2022 (Median) to 12 May 2025, with 802 observations. The data reached an all-time high of 1,027.170 BRL in 27 Dec 2023 and a record low of 764.780 BRL in 02 Jan 2025. Government Bond Yield: Daily: 10 Years: Maturity 2033: Sale Price Morning data remains active status in CEIC and is reported by National Treasury Secretariat. The data is categorized under Brazil Premium Database’s Interest and Foreign Exchange Rates – Table BR.MI007: Tesouro Direto: Government Bonds Yield: by Maturity: 2033.

Government Bond Yield: Daily: 10 Years: Maturity 2035: Purchase Price Morning data was reported at 845.300 BRL in 12 May 2025. This records an increase from the previous number of 844.000 BRL for 09 May 2025. Government Bond Yield: Daily: 10 Years: Maturity 2035: Purchase Price Morning data is updated daily, averaging 899.350 BRL from Feb 2024 (Median) to 12 May 2025, with 313 observations. The data reached an all-time high of 979.710 BRL in 07 Mar 2024 and a record low of 750.960 BRL in 02 Jan 2025. Government Bond Yield: Daily: 10 Years: Maturity 2035: Purchase Price Morning data remains active status in CEIC and is reported by National Treasury Secretariat. The data is categorized under Brazil Premium Database’s Interest and Foreign Exchange Rates – Table BR.MI008: Tesouro Direto: Government Bonds Yield: by Maturity: 2035.

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.

Rise and Fall of SRBK: A Tale of Two Banks?

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

Government Bond Yield: Daily: 10 Years: Maturity 2031: Purchase Price Morning data was reported at 898.310 BRL in 12 May 2025. This records an increase from the previous number of 897.520 BRL for 09 May 2025. Government Bond Yield: Daily: 10 Years: Maturity 2031: Purchase Price Morning data is updated daily, averaging 965.900 BRL from Feb 2020 (Median) to 12 May 2025, with 1306 observations. The data reached an all-time high of 1,272.150 BRL in 17 Feb 2020 and a record low of 798.170 BRL in 02 Jan 2025. Government Bond Yield: Daily: 10 Years: Maturity 2031: Purchase Price Morning data remains active status in CEIC and is reported by National Treasury Secretariat. The data is categorized under Brazil Premium Database’s Interest and Foreign Exchange Rates – Table BR.MI005: Tesouro Direto: Government Bonds Yield: by Maturity: 2031.

The yield on France 10Y Bond Yield eased to 3.27% on July 4, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0 points and is 0.10 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. France 10-Year Government Bond Yield - 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.

Newmark Rise or Fall: NMRK Stock's Future?

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.

Machine Learning Predicts QQQ to Increase in Value by 5% in the Next 3 Months

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 yield on Australia 10Y Bond Yield rose to 4.19% on July 4, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.05 points and is 0.22 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Australia 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on Belgium 10Y Bond Yield rose to 3.16% on July 4, 2025, marking a 0 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.03 points and is 0.04 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Belgium 10-Year Government Bond Yield - 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.

Potbelly (PBPB) Bread & Butter: Is This Stock Ready to 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

The yield on US 3 Year Note Bond Yield rose to 3.85% on July 3, 2025, marking a 0.08 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.01 points, though it remains 0.65 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 3 Year Note Yield - values, historical data, forecasts and news - updated on July of 2025.

The yield on Germany 10Y Bond Yield eased to 2.57% on July 1, 2025, marking a 0.03 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.04 points, though it remains 0.04 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Germany 10-Year Bond Yield - 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.

Is INDB stock expected to 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