The yield on US 10 Year Note Bond Yield rose to 4.37% on July 23, 2025, marking a 0.02 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.07 points and is 0.08 points higher 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.

The Global Bond Market is Segmented by Type (Treasury Bonds, Municipal Bonds, Corporate Bonds, High-Yield Bonds, Mortgage-Backed Securities, and More), by Issuer (Public Sector Issuers, Private Sector Issuers), by Sectors (Energy and Utilities, Technology, Media and Telecom, Healthcare, Consumers, Industrial, Real Estate and More), and Region. The Market Forecasts are Provided in Terms of Value (USD).

The yield on US 30 Year Bond Yield rose to 4.94% on July 23, 2025, marking a 0.02 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.10 points and is 0.39 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.

In June 2025, the yield on a 10-year U.S. Treasury note was **** percent, forecasted to decrease to reach **** percent by February 2026. Treasury securities are debt instruments used by the government to finance the national debt. Who owns treasury notes? Because the U.S. treasury notes are generally assumed to be a risk-free investment, they are often used by large financial institutions as collateral. Because of this, billions of dollars in treasury securities are traded daily. Other countries also hold U.S. treasury securities, as do U.S. households. Investors and institutions accept the relatively low interest rate because the U.S. Treasury guarantees the investment. Looking into the future Because these notes are so commonly traded, their interest rate also serves as a signal about the market’s expectations of future growth. When markets expect the economy to grow, forecasts for treasury notes will reflect that in a higher interest rate. In fact, one harbinger of recession is an inverted yield curve, when the return on 3-month treasury bills is higher than the ten-year rate. While this does not always lead to a recession, it certainly signals pessimism from financial markets.

Global bond market was valued at USD 141.34 Trillion in 2024 and is expected to reach USD 166.81 Trillion by 2030 with a CAGR of 2.8% during the forecast period.

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.

What are bonds in stocks?

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 Japan 10Y Bond Yield rose to 1.59% on July 23, 2025, marking a 0.09 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.17 points and is 0.52 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.

Predictions and Risks for Stifel Financial Corporation 5.20% Senior Notes due 2047: Fixed income markets remain volatile amidst rising interest rates, affecting bond prices. Stifel Financial Corporation's strong financial position and consistent dividend payments indicate resilience but fluctuations in interest rates pose risks to bond value. The company's exposure to economic downturns and regulatory changes can impact cash flows and the ability to meet debt obligations. Investors should consider the potential for interest rate fluctuations, economic headwinds, and regulatory challenges when assessing the risk and potential returns of the bonds.

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.

ESG: Advanced Choice for Corporate Bond Investing?

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 Global Green Bonds Market is Segmented by Issuer Type (Sovereigns, Supranationals & Agencies, Financial Corporates, Non-Financial Corporates, and Municipal & Local Authorities), Use-Of-Proceeds Sector (Energy, Buildings, Transport, Water & Wastewater, and More), Bond Format (Senior Unsecured, Asset-backed/Project Bond, Covered Bond, and More), and Geography. The Market Forecasts are Provided in Value (USD).

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.

Entergy's (ENJ) 5% Bonds: A Long-Term Haven?

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 July 22, 2025, the yield for a ten-year U.S. government bond was 4.38 percent, while the yield for a two-year bond was 3.88 percent. This represents an inverted yield curve, whereby bonds of longer maturities provide a lower yield, reflecting investors' expectations for a decline in long-term interest rates. Hence, making long-term debt holders open to more risk under the uncertainty around the condition of financial markets in the future. That markets are uncertain can be seen by considering both the short-term fluctuations, and the long-term downward trend, of the yields of U.S. government bonds from 2006 to 2021, before the treasury yield curve increased again significantly in the following years. What are government bonds? Government bonds, otherwise called ‘sovereign’ or ‘treasury’ bonds, are financial instruments used by governments to raise money for government spending. Investors give the government a certain amount of money (the ‘face value’), to be repaid at a specified time in the future (the ‘maturity date’). In addition, the government makes regular periodic interest payments (called ‘coupon payments’). Once initially issued, government bonds are tradable on financial markets, meaning their value can fluctuate over time (even though the underlying face value and coupon payments remain the same). Investors are attracted to government bonds as, provided the country in question has a stable economy and political system, they are a very safe investment. Accordingly, in periods of economic turmoil, investors may be willing to accept a negative overall return in order to have a safe haven for their money. For example, once the market value is compared to the total received from remaining interest payments and the face value, investors have been willing to accept a negative return on two-year German government bonds between 2014 and 2021. Conversely, if the underlying economy and political structures are weak, investors demand a higher return to compensate for the higher risk they take on. Consequently, the return on bonds in emerging markets like Brazil are consistently higher than that of the United States (and other developed economies). Inverted yield curves When investors are worried about the financial future, it can lead to what is called an ‘inverted yield curve’. An inverted yield curve is where investors pay more for short term bonds than long term, indicating they do not have confidence in long-term financial conditions. Historically, the yield curve has historically inverted before each of the last five U.S. recessions. The last U.S. yield curve inversion occurred at several brief points in 2019 – a trend which continued until the Federal Reserve cut interest rates several times over that year. However, the ultimate trigger for the next recession was the unpredicted, exogenous shock of the global coronavirus (COVID-19) pandemic, showing how such informal indicators may be grounded just as much in coincidence as causation.

Green Bond Market size was valued at around USD 224 billion in 2024 and is projected to reach USD 350 billion by 2030, growing at a CAGR of 8%.

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.

Global green bond market was valued at USD 653.89 Billion in 2024 and is expected to reach USD 1026.17 Billion by 2030 with a CAGR of 7.8% during the forecast period.

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.

VIX: Fear or Opportunity?

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

Bond Fund Sales Market Outlook

The bond fund sales market size was valued at approximately USD 10 trillion in 2023 and is projected to reach around USD 15 trillion by 2032, growing at a compound annual growth rate (CAGR) of 4.5%. This growth is primarily driven by increasing investor demand for stable and diversified income streams amidst global economic uncertainties. The market size expansion is fostered by factors such as an aging global population seeking more conservative investment options, heightened volatility in equity markets, and favorable regulatory changes supporting bond fund investments.

One of the primary growth factors for the bond fund sales market is the demographic shift towards an aging population, particularly in developed regions such as North America and Europe. As more individuals approach retirement age, there is a heightened need for investment products that offer steady income with reduced risk exposure. Bond funds, known for their relatively stable returns and lower volatility compared to equity funds, serve as an attractive option for this demographic. Additionally, the increasing life expectancy rates globally are pushing retirees to seek long-term investment solutions that can provide consistent income streams over extended periods.

Another significant growth driver is the evolving regulatory landscape that favors bond investments. Governments and financial regulatory bodies in various regions are implementing rules and guidelines that promote transparency and investor protection in the bond markets. These regulatory changes increase investor confidence and make bond funds more appealing to both retail and institutional investors. Furthermore, the introduction of green bonds and other socially responsible investment (SRI) products within the bond fund market is drawing interest from a growing segment of environmentally and socially conscious investors.

Technological advancements and the proliferation of digital investment platforms are also contributing to the growth of the bond fund sales market. Online platforms and robo-advisors are making it easier for retail investors to access and manage bond fund investments with lower fees and greater convenience. These platforms provide investors with tools and resources to make informed investment decisions, thereby increasing the participation rate of individual investors in the bond market. This digital transformation is democratizing access to bond funds and expanding the market's reach across various investor segments.

Regionally, the bond fund sales market exhibits diverse growth patterns. North America and Europe are expected to maintain their dominance due to their mature financial markets and high levels of investor awareness and engagement. However, the Asia-Pacific region is anticipated to exhibit the highest CAGR during the forecast period, driven by rapid economic growth, rising disposable incomes, and increasing investor sophistication. Latin America and the Middle East & Africa regions are also witnessing growing interest in bond funds, albeit at a slower pace, as these markets gradually develop and integrate into the global financial system.

Fund Type Analysis

Government bond funds are a cornerstone of the bond fund market, offering investors a relatively low-risk investment option backed by government securities. These funds have been traditionally appealing to risk-averse investors, including retirees and conservative institutional investors. The demand for government bond funds is amplified during periods of economic uncertainty, as they are perceived as safe havens. The increasing issuance of government bonds to finance fiscal stimulus and infrastructure projects globally is also contributing to the growth of this segment. Moreover, central banks' policies, such as quantitative easing, have increased the liquidity and attractiveness of these bonds.

Corporate bond funds represent a significant portion of the bond fund market, providing higher yields compared to government bonds, albeit with increased risk. These funds invest in bonds issued by corporations to finance their operations and expansions. The corporate bond market is highly dynamic, with companies frequently entering and exiting the market based on their financing needs and credit ratings. The growth of this segment is supported by strong corporate earnings and favorable economic conditions that enhance companies' ability to service their debt. Additionally, the trend towards globalization and cross-border investments is expanding the market for corporate bond funds.

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 iShares U.S. Treasury Bond ETF the Right Choice for Your Portfolio?

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 States FRBOP Forecast: Treasury Bonds Rate: 10 Years: Mean: Plus 3 Qtrs data was reported at 3.363 % in Jun 2018. This records an increase from the previous number of 3.113 % for Mar 2018. United States FRBOP Forecast: Treasury Bonds Rate: 10 Years: Mean: Plus 3 Qtrs data is updated quarterly, averaging 4.927 % from Mar 1992 (Median) to Jun 2018, with 106 observations. The data reached an all-time high of 7.891 % in Dec 1994 and a record low of 2.060 % in Sep 2012. United States FRBOP Forecast: Treasury Bonds Rate: 10 Years: Mean: Plus 3 Qtrs data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.M006: Treasury Bills Rates: Forecast: Federal Reserve Bank of Philadelphia.

Global Bond market size 2021 was recorded $13139.1 Billion whereas by the end of 2025 it will reach $14872.8 Billion. According to the author, by 2033 Bond market size will become $19056.6. Bond market will be growing at a CAGR of 3.147% during 2025 to 2033.