The yield on US 10 Year Note Bond Yield rose to 4.41% on July 11, 2025, marking a 0.06 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.04 points and is 0.23 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 yield on US 30 Year Bond Yield rose to 4.96% on July 11, 2025, marking a 0.09 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.11 points and is 0.56 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 China 10Y Bond Yield rose to 1.66% on July 11, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has edged up by 0 points, though it remains 0.60 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.

The Worldwide Fixed Income (WFI) Service enables you to keep track of new bond issues or changes in terms and conditions for both corporate and government issuances. Data is sourced globally from stock exchanges, central banks, ministries of finance, lead managers, paying, calculation and transfer agents.

The fixed income data service cover 40 event types including redemption, conversion, defaults and contains static data outlining key terms and conditions and call schedules. EDI can provide you with pricing supplements, offering circulars, term sheets and prospectuses for as many securities as possible subject to availability. It covers approximately 30% of the Fixed Income database. Use cases: Bond Issuance Tracking | Portfolio Risk Management | Portfolio Valuation | Investment Management | Market Analysis

With the service you will have access to: -International debt securities in more than 150 countries A broad range of asset types including: -Convertibles -FRNs -Permanent interest bearing shares -Preferred securities -Treasury bills In addition, where possible we can extend both instruments and geographic coverage to fully cover your portfolio.

Originally in the equity space, Exchange Data International (EDI) moved to the Fixed Income arena following an increased demand from clients to add debt instruments to its coverage. As the firm was approached by a major credit rating agency to build a customised fixed income service, it developed its own Fixed Income service providing global coverage of the debt market. New countries and sources are continually researched and added to enhance geographic coverage and increase the volume of securities in the database. The service provides historical data back from 2007.

Asset Classes Fully covered: • Canadian strip packages without underlying • Cash management bills • Certificate of deposit (tenure more than 28 days) • Commercial papers (tenure more than 28 days) • Convertibles • Corporate bonds • Government bonds • Municipal securities • Short-term corporate Bonds • Short-term government Bonds • Strips (parent needed) • Treasury bills

Covered if in portfolio: • Asset-backed securities (ABS) (securities entered with critical fields and just covered for live • client’s portfolio and Canada; offering documents processed for live clients; corporate actions not maintained) • Certificates (just covered for live client’s portfolio) • Mortgage-backed securities (MBS) (securities entered with critical fields and just covered for live client’s portfolio and Canada, offering documents processed for live clients; corporate actions not maintained) • Musharaka Sukuks (securities entered with critical fields and just covered for live client’s Portfolio; offering documents processed for live clients; corporate actions not maintained) • Structured Products • Genussschein (AT, CH and DE) • Mortgage-pass through certificates • Pass-through certificates In addition, EDI provides a comprehensive global Fixed Income Corporate Action/Event service, to compliment the reference data, including security and issuer level events and distributions.

Quarterly positions in debt securities issues by sector, currency, maturity, type of interest rate and market of issuance, positions at the end of the quarter, book and market value.

Browse LSEG's Debt New Issues Deals Data, providing industry-leading debt transaction information and league tables to the global deal-making industry.

Brazil New Issuance: Corporate Bonds: CVM: Number of Issues data was reported at 0.000 Unit in Apr 2025. This stayed constant from the previous number of 0.000 Unit for Mar 2025. Brazil New Issuance: Corporate Bonds: CVM: Number of Issues data is updated monthly, averaging 1.000 Unit from Jan 1995 (Median) to Apr 2025, with 364 observations. The data reached an all-time high of 18.000 Unit in Dec 1995 and a record low of 0.000 Unit in Apr 2025. Brazil New Issuance: Corporate Bonds: CVM: Number of Issues data remains active status in CEIC and is reported by Securities and Exchange Commission of Brazil. The data is categorized under Global Database’s Brazil – Table BR.ZA014: Primary Market.

The yield on Japan 10Y Bond Yield rose to 1.51% on July 11, 2025, marking a 0.01 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.05 points and is 0.46 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.

Digital Bond Issuance Platform Market Outlook

According to our latest research, the global Digital Bond Issuance Platform market size reached USD 1.92 billion in 2024, demonstrating robust momentum fueled by the ongoing digital transformation in the financial sector. The market is projected to expand at a compound annual growth rate (CAGR) of 17.4% during the forecast period, reaching a value of USD 5.65 billion by 2033. This remarkable growth is primarily driven by increasing adoption of blockchain technology, rising demand for efficient and transparent bond issuance processes, and the growing need for cost reduction and operational efficiency in capital markets.

The primary growth factor for the Digital Bond Issuance Platform market is the accelerating shift towards digitalization in the global financial ecosystem. Traditional bond issuance processes are often characterized by manual procedures, high costs, and extended timelines, which have become increasingly unsustainable in today’s fast-paced market environment. Digital bond issuance platforms leverage advanced technologies such as blockchain, smart contracts, and automated workflows to streamline the entire bond lifecycle, from origination and issuance to settlement and ongoing management. These platforms offer significant benefits, including enhanced transparency, real-time tracking, reduced risk of errors, and improved compliance with regulatory requirements, making them highly attractive to financial institutions, corporates, and government entities seeking to modernize their capital-raising activities.

Another key driver of market growth is the rising popularity of green bonds and other sustainable finance instruments. As investors and regulators place greater emphasis on environmental, social, and governance (ESG) criteria, there is a growing demand for digital solutions that can support the efficient issuance and management of green bonds and similar instruments. Digital bond issuance platforms are uniquely positioned to meet this need by providing robust frameworks for tracking ESG metrics, ensuring transparency in the use of proceeds, and facilitating reporting to stakeholders. This trend is expected to further accelerate market expansion, particularly as governments and corporations worldwide intensify their efforts to address climate change and promote sustainable development.

The increasing integration of digital bond issuance platforms with existing financial infrastructure and the emergence of new regulatory frameworks are also contributing to market growth. Financial authorities in leading markets such as Europe, North America, and Asia Pacific are actively exploring and implementing regulations that support the adoption of digital securities and distributed ledger technology (DLT) in bond markets. This regulatory support, combined with the growing ecosystem of technology providers, financial institutions, and market participants, is fostering innovation and enabling the development of more sophisticated and user-friendly digital bond issuance solutions. As a result, the market is witnessing a steady influx of new entrants and strategic partnerships, further enhancing its growth prospects.

Regionally, North America and Europe are leading the adoption of digital bond issuance platforms, driven by their mature financial markets, advanced technological infrastructure, and proactive regulatory environments. However, Asia Pacific is rapidly emerging as a key growth region, fueled by increasing digitalization initiatives, expanding capital markets, and supportive government policies. Latin America and the Middle East & Africa are also showing promising potential, particularly as local financial institutions seek to modernize their operations and tap into new sources of capital. Overall, the global digital bond issuance platform market is poised for significant expansion, with strong growth opportunities across all major regions.

Component Analysis

The Component segment of the digital bond iss

Brazil New Issuance: Corporate Bonds: CVM: Traded Value: USD data was reported at 0.000 USD mn in Apr 2025. This stayed constant from the previous number of 0.000 USD mn for Mar 2025. Brazil New Issuance: Corporate Bonds: CVM: Traded Value: USD data is updated monthly, averaging 154.936 USD mn from Jan 1995 (Median) to Apr 2025, with 364 observations. The data reached an all-time high of 15.099 USD bn in Jul 2006 and a record low of 0.000 USD mn in Apr 2025. Brazil New Issuance: Corporate Bonds: CVM: Traded Value: USD data remains active status in CEIC and is reported by Securities and Exchange Commission of Brazil. The data is categorized under Global Database’s Brazil – Table BR.ZA014: Primary 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.

Entergy New Orleans 5.0% Bonds (ENJ) - A Stable Bet for Long-Term Investors?

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

South Africa Bond Market: New Stock Issues: Public Sector data was reported at 13,690.000 ZAR mn in Oct 2018. This records an increase from the previous number of 13,598.000 ZAR mn for Sep 2018. South Africa Bond Market: New Stock Issues: Public Sector data is updated monthly, averaging 2,248.000 ZAR mn from Jan 1982 (Median) to Oct 2018, with 442 observations. The data reached an all-time high of 24,764.000 ZAR mn in Mar 2018 and a record low of -24,914.000 ZAR mn in Jan 2002. South Africa Bond Market: New Stock Issues: Public Sector data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z013: Johannesburg Stock Exchange: Bond 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.

Ishares USD Green Bond ETF: Greening the Bond 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

As of September 2020, the European countries with the highest volume of green bonds issued by companies debuting on the green bond market were the Netherlands, Norway and the UK, each with **** issuers. In 2019 this honor went to Sweden, with ** issuers. Green bonds are fixed income securities which finance investments with environmental or climate-related benefits.

These rates are the daily secondary market quotation on the most recently auctioned Treasury Bills for each maturity tranche (4-week, 13-week, 26-week, and 52-week) that Treasury currently issues new Bills. Market quotations are obtained at approximately 3:30 PM each business day by the Federal Reserve Bank of New York. The Bank Discount rate is the rate at which a Bill is quoted in the secondary market and is based on the par value, amount of the discount and a 360-day year. The Coupon Equivalent, also called the Bond Equivalent, or the Investment Yield, is the bill's yield based on the purchase price, discount, and a 365- or 366-day year. The Coupon Equivalent can be used to compare the yield on a discount bill to the yield on a nominal coupon bond that pays semiannual interest.

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 New Orleans (ENOstock) Bonds: A Steady Stream of Income

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.

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

These rates are commonly referred to as Constant Maturity Treasury rates, or CMTs. Yields are interpolated by the Treasury from the daily yield curve. This curve, which relates the yield on a security to its time to maturity is based on the closing market bid yields on actively traded Treasury securities in the over-the-counter market. These market yields are calculated from composites of quotations obtained by the Federal Reserve Bank of New York. The yield values are read from the yield curve at fixed maturities, currently 1, 3 and 6 months and 1, 2, 3, 5, 7, 10, 20, and 30 years. This method provides a yield for a 10 year maturity, for example, even if no outstanding security has exactly 10 years remaining to maturity.

The yield on Canada 10Y Bond Yield rose to 3.53% on July 14, 2025, marking a 0.03 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.13 points and is 0.11 points higher 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.

Graph and download economic data for Market Value of Marketable Treasury Debt (MVMTD027MNFRBDAL) from Jan 1942 to Jun 2025 about market value, debt, Treasury, and USA.