Graph and download economic data for from Jan 1948 to Jan 1967 about bonds, yield, interest rate, interest, rate, 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.

Graph and download economic data for Index of Yields of High Grade Corporate and Municipal Bonds for United States (M13021USM156NNBR) from Jan 1900 to Dec 1967 about grades, bonds, corporate, yield, interest rate, interest, rate, indexes, and USA.

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.

The yield on US 30 Year Bond Yield rose to 4.92% on August 15, 2025, marking a 0.05 percentage point increase from the previous session. Over the past month, the yield has fallen by 0.09 points, though it remains 0.78 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 Bond Buyer Go 20-Bond Municipal Bond Index (DISCONTINUED) (WSLB20) from 1953-01-01 to 2016-10-06 about municipal, state & local, bonds, government, indexes, and USA.

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.

Index Time Series for Overlay Shares Municipal Bond ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund is an actively-managed exchange-traded fund (ETF) that seeks to achieve its objective by (i) investing in one or more other ETFs that seek to obtain exposure to the performance of investment grade municipal bonds and below investment grade municipal bonds or directly in the securities held by such ETFs and (ii) selling and purchasing listed short-term put options to generate income to the fund.

Carbon-Smart Municipal Bond Market Outlook

According to our latest research, the global carbon-smart municipal bond market size reached USD 68.4 billion in 2024, demonstrating robust momentum driven by the increasing emphasis on sustainable finance and climate-resilient infrastructure. The market is registering a compelling compound annual growth rate (CAGR) of 13.7% and is expected to reach USD 202.6 billion by 2033. This growth is attributed to mounting regulatory pressure, heightened investor demand for ESG-compliant assets, and an urgent need for municipalities to finance projects that align with net-zero carbon objectives.

A key growth factor for the carbon-smart municipal bond market is the global policy shift towards decarbonization and the adoption of sustainable development goals (SDGs). Governments and regulatory bodies worldwide are increasingly mandating transparency in climate-related financial disclosures and encouraging municipalities to finance low-carbon projects through innovative debt instruments. This regulatory landscape is fostering a conducive environment for the proliferation of carbon-smart municipal bonds, which are specifically structured to fund projects that reduce greenhouse gas emissions or enhance climate resilience. Furthermore, the growing alignment of municipal investment strategies with the Paris Agreement is prompting issuers to adopt carbon-smart criteria, further accelerating market expansion.

Investor appetite for sustainable and impact-driven investments is also fueling the rapid growth of the carbon-smart municipal bond market. Institutional investors, such as pension funds, insurance companies, and asset managers, are under mounting pressure to integrate environmental, social, and governance (ESG) considerations into their portfolios. This demand is being met by municipalities issuing bonds that explicitly target carbon reduction and sustainable infrastructure development. Additionally, the proliferation of green and social bonds within the municipal finance sector is providing investors with clearly defined metrics for evaluating the environmental and social impact of their investments, thereby driving capital flows into carbon-smart municipal bonds.

Another critical driver is the escalating need for municipalities to upgrade and expand their infrastructure in a manner that addresses both climate adaptation and mitigation. Aging transportation networks, water systems, and energy grids are increasingly vulnerable to climate risks, necessitating significant investments in resilient, low-carbon solutions. Carbon-smart municipal bonds offer an attractive financing mechanism for these projects, allowing issuers to tap into a growing pool of climate-conscious investors. The integration of carbon measurement and reporting standards into municipal bond structures is further enhancing investor confidence and facilitating the mainstreaming of carbon-smart finance.

From a regional perspective, North America and Europe are leading the adoption of carbon-smart municipal bonds, owing to advanced regulatory frameworks, high investor awareness, and robust municipal finance markets. However, emerging economies in Asia Pacific and Latin America are rapidly catching up, propelled by urbanization, infrastructure deficits, and increasing exposure to climate-related risks. These regions are witnessing a surge in municipal bond issuances aimed at financing green infrastructure and sustainable urban development, signaling a broad-based global expansion of the carbon-smart municipal bond market.

Bond Type Analysis

The carbon-smart municipal bond market is segmented by bond type into general obligation bonds, revenue bonds, green bonds, social bonds, and others. General obligation bonds, traditionally backed by the full faith and credit of the issuing municipality, are increasingly being structured with carbon-smart criteria. These bonds finance a wide range of public projects, including those that contribute to reduced carbon emissions, such as energy-efficient public buildings and sustainable community development. The incorporation of carbon performance metrics into general obligation bonds is attracting a broader spectrum of ESG-focused investors, thereby enhancing market liquidity and pricing efficiency.

Revenue bonds, which are repaid from specific revenue streams generated by the financed proj

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 Mutual Funds; Total Financial Assets in Municipal Bond Funds, Transactions (BOGZ1FA654091203Q) from Q1 1991 to Q1 2025 about municipal, mutual funds, transactions, bonds, assets, and USA.

The SIFMA Municipal Swap Index, formerly the Bond Market Association Index, is a market index composed of tax-exempt variable rate demand obligations (VRDOs). VRDOs are municipal bonds with floating interest rates. The SIFMA index is issued weekly.

The SIFMA rate for each interest payment period is equal to the weighted average of the SIFMA index value. Both SIFMA and LIBOR are popular floating rate index. The SIFMA rate represents the average interest rate payable on tax-exempt variable rate demand obligations, while the LIBOR rate represents the interest rate payable on non-tax exempt demand obligations. In general, the SIFMA rate trades as a proportion of LIBOR rate.

The coupon rates of many floating rate bonds or floating rate callable bonds refer to SIFMA index. The change of index has quite impact on the bond values. Thus, the SIFMA curve is major used to price various bonds, such as municipal bonds, municipal debts, bond purchase agreements, etc.

Index Time Series for SSGA Active Trust - SPDR Nuveen Municipal Bond ESG ETF. The frequency of the observation is daily. Moving average series are also typically included. Under normal circumstances, the fund invests at least 80% of its net assets (plus the amount of borrowings for investment purposes) in municipal bonds that pay income that is exempt from regular federal income tax. Under normal market conditions, its investment portfolio will consist primarily of municipal bonds rated Baa3/BBB-/BBB- - or higher by Moody's, S&P or Fitch Ratings, Inc., respectively. It is non-diversified.

Dataset characteristics for the whole market data and water and sewer revenue bonds only, for both response variables: market spread and spread at issue.

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 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

Graph and download economic data for State and Local Governments; Municipal Bond Offering for Refunding; Liability, Transactions (BOGZ1FA213162703A) from 1970 to 2024 about municipal, retirement, state & local, transactions, liabilities, bonds, government, employment, and USA.

A $1.15 billion municipal bond sale will fund a new tire factory in Oklahoma, offering high-yield, tax-free bonds to qualified investors.

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.

BHV BlackRock Virginia Municipal Bond Trust

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 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).