Graph and download economic data for Yields on Municipal Bonds, Twenty Bond Average for United States (M13050USM156NNBR) from Jan 1948 to Jan 1967 about bonds, yield, interest rate, interest, rate, and USA.

The yield on US 30 Year Bond Yield eased to 4.84% on August 1, 2025, marking a 0.06 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.02 points and is 0.73 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 August of 2025.

Graph and download economic data for Index of Yields of High Grade Municipal Bonds for United States (M13023USM156NNBR) from Jan 1900 to Apr 1967 about grades, bonds, yield, interest rate, interest, rate, indexes, and USA.

The Mergent Municipal Bond Securities database provides information on U.S. domestic municipal bonds beginning in 1996. It covers municipal issues from all 50 states including bonds issued by states, counties, and cities as well as other municipal entities such as hospitals, community colleges, schools, water districts, and other similar entities. Data is at issue level for all individual bonds including the underwriter, bond yield, offering price, offering date, maturity, and other bond characteristics (e.g., taxable, security, use of proceeds, sale type, refunding). It also includes information on credit ratings at issuance and throughout the life of the bond from S&P, Moody’s, and Fitch. Each bond has a maturity_id and issue_id that allows for matching across tables within the Mergent dataset. The full 9-digit CUSIP for each bond is also provided. There is some coverage for geographic areas outside of the 50 states (e.g., Puerto Rico and the Virgin Islands). It also includes some bonds issued prior to 1996, and some debt instruments other than public bonds (e.g., collateralized notes, certificates of obligation, construction loan notes). However, the extent of coverage for these additional geographic areas, offering dates, and debt instruments is unknown, suggesting that researchers exercise caution before using these data. Data is current to December 2024.

This dataset compiles national-level municipal bond issuance and pricing statistics for the United States, sourced from the Securities Industry and Financial Markets Association (SIFMA). It includes time-series data on municipal bond issuance volumes, average yields, interest rates, and maturity structures, aggregated on a monthly and annual basis. The dataset provides critical macro-financial context for evaluating subnational debt trends, especially in the context of climate adaptation investments and fiscal resilience. In particular, it supports comparative analysis between local climate-related borrowing (e.g., FEMA-backed projects) and national municipal debt trends, serving as a benchmark for assessing changes in risk premiums, cost of capital, and investor behavior. This file was used to calibrate yield spreads in empirical models evaluating the market response to federally co-funded nature-based infrastructure.

This folder contains the replication code for "Sea Level Rise Exposure and Municipal Bond Yields" The main analysis code is Code/analysis.do The data file for running the code is called Data/final_regression_data.dta. However, we only include an example row of the data because the municipal bond data is proprietary. Users interested in constructing the dataset would need to purchase the bond data from Mergent. However, we do include our SLR projections data in Data/SLR Projections As well as the cross-walk between districts and issuer names matched_issuer_districts_clean.csv

Both climate risk and race are factors that may affect municipal bond yields, yet each has received relatively limited empirical research attention. We analyzed > 712,000 municipal bonds representing nearly 2 trillion USD in par outstanding, focusing on credit spread or the difference between a debt issuer’s interest cost to borrow and a benchmark “risk-free” municipal rate. The relationship between credit spread and physical climate risk is significant and slightly positive, yet the coefficient indicates no meaningful spread penalty for increased physical climate risk. We also find that racial composition (the percent of a community that is Black) explains a statistically significant and meaningful portion of municipal credit spreads, even after controlling for a variety of variables in domains such as geographic location of issuer, bond structure (e.g., bond maturity), credit rating, and non-race economic variables (e.g., per capita income). Assuming 4 trillion USD in annual outstanding par across the entire municipal market, and weighting each issuer by its percent Black, an estimated 19 basis point (bp) penalty for Black Americans sums to approximately 900 million USD annually in aggregate. Our combined findings indicate a systemic mispricing of risk in the municipal bond market, where race impacts the cost of capital, and climate does not.

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.

ENX: Steady Returns in the Municipal 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

Historical dividend payout and yield for BNY Mellon Strategic Municipal Bond Fund (DSM) since 1992. The current TTM dividend payout for BNY Mellon Strategic Municipal Bond Fund (DSM) as of June 25, 2025 is $0.18. The current dividend yield for BNY Mellon Strategic Municipal Bond Fund as of June 25, 2025 is 3.27%.

Historical dividend payout and yield for SPDR Nuveen Bloomberg Municipal Bond ETF (TFI) since 2009. The current TTM dividend payout for SPDR Nuveen Bloomberg Municipal Bond ETF (TFI) as of June 30, 2025 is $1.40. The current dividend yield for SPDR Nuveen Bloomberg Municipal Bond ETF as of June 30, 2025 is 3.16%.

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).

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.

BNY Mellon Municipal Bond Infrastructure Fund Inc. (DMB): Municipals: Paving the Way to Financial Strength?

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.

NAC: Headed for Municipal Bond Market Recovery?

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 (BAA) from Jan 1919 to Jun 2025 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.

DMB BNY Mellon Municipal Bond Infrastructure Fund Inc. Common Stock

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

Get Bond Analytics from LSEG to better analyze government and corporate bonds, preferred shares, inflation-linked bonds and municipal bonds. Find out more.

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.

BBN BlackRock Taxable Municipal Bond Trust Common Shares of Beneficial Interest

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 dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

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Fixed Income Assets Management Market Size 2025-2029

The fixed income assets management market size is forecast to increase by USD 9.16 tr at a CAGR of 6.3% between 2024 and 2029.

The market is experiencing significant growth, driven by increasing investor interest in fixed income securities as a hedge against market volatility. A key trend in this market is the expansion of bond Exchange-Traded Funds (ETFs), which offer investors liquidity, diversification, and cost savings. However, this market is not without risks. Transactions in fixed income assets involve complexities such as credit risk, interest rate risk, and liquidity risk, which require sophisticated risk management strategies. As global investors seek to capitalize on market opportunities and navigate these challenges effectively, they must stay informed of regulatory changes, market trends, and technological advancements. Companies that can provide innovative solutions for managing fixed income risks and optimizing returns will be well-positioned to succeed in this dynamic market.

What will be the Size of the Fixed Income Assets Management Market during the forecast period?

Request Free SampleThe fixed income assets market in the United States continues to be an essential component of investment portfolios for various official institutions and individual investors. With an expansive market size and growth, fixed income securities encompass various debt instruments, including corporate bonds and government treasuries. Interest rate fluctuations significantly impact this market, influencing investment decisions and affecting the returns from interest payments on these securities. Fixed income Exchange-Traded Funds (ETFs) and index managers have gained popularity due to their cost-effective and diversified investment options. However, the credit market volatility and associated default risk pose challenges for investors. In pursuit of financial goals, investors often choose fixed income funds over equities for their stable dividend income and tax savings benefits. Market risk and investors' risk tolerance are crucial factors in managing fixed income assets. Economic uncertainty and interest rate fluctuations necessitate active management by asset managers, hedge funds, and mutual funds. The fund maturity and investors' financial goals influence the choice between various fixed income securities, such as treasuries and loans. Despite the challenges, the market's direction remains positive, driven by the continuous demand for income-generating investments.

How is this Fixed Income Assets Management Industry segmented?

The fixed income assets management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD tr' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeCoreAlternativeEnd-userEnterprisesIndividualsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaSouth AmericaMiddle East and Africa

By Type Insights

The core segment is estimated to witness significant growth during the forecast period.The fixed income asset management market encompasses a diverse range of investment vehicles, including index investing, pension funds, official institutions, mutual funds, investment advisory services, and hedge funds. This asset class caters to income holders with varying risk tolerances, offering securities such as municipal bonds, government bonds, and high yield bonds through asset management firms. Institutional investors, insurance companies, and corporations also play significant roles in this sector. Fixed income securities, including Treasuries, municipal bonds, corporate bonds, and debt securities, provide regular interest payments and can offer tax savings, making them attractive for investors with financial goals. However, liquidity issues and credit market volatility can pose challenges. The Federal Reserve's interest rate decisions and economic uncertainty also impact the fixed income market. Asset management firms employ various strategies, such as the core fixed income (CFI) strategy, which invests in a mix of investment-grade fixed-income securities. CFI strategies aim to deliver consistent performance by carefully managing portfolios, considering issuer creditworthiness, maturity, and jurisdiction. Fixed income funds, including government bonds and corporate bonds, offer lower market risk compared to equities. Investors can choose from various investment vehicles, including mutual funds, ETFs, and index funds managed by active managers or index managers. Fixed income ETFs, in particular, provide investors with the benefits of ETFs, such as liquidity and transparency, while offering exposure to the fixed income market. Despite market risks and liquidity issues, the fixed income asset management market continues to be