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

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, yield, corporate, interest rate, interest, rate, indexes, and USA.

The yield on US 30 Year Bond Yield eased to 4.99% on July 18, 2025, marking a 0.02 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.10 points and is 0.54 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.

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 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. The data provides information about the bond issue and the individual bonds within each bond issue, 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. Information is provided at the bond issue level (issue_id) and at the bond level using the maturity_id. 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 May 5, 2023.

Graph and download economic data for Bond Buyer Go 20-Bond Municipal Bond Index (DISCONTINUED) (MSLB20) from Jan 1953 to Sep 2016 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.

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.

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

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

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.

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

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

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

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.

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.

RiverNorth Flexible Municipal Income Fund II (RFMZ) Forecast: Navigating the Municipal Bond Landscape

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).

Fixed Income Asset Management Market Outlook

The global fixed income asset management market size was valued at approximately USD 5.7 trillion in 2023 and is projected to grow to USD 9.3 trillion by 2032, expanding at a compound annual growth rate (CAGR) of 5.5% over the forecast period. The growth of this market is primarily driven by the increasing demand for stable and predictable returns in an uncertain economic environment.

One of the significant growth factors for the fixed income asset management market is the aging global population. As more individuals approach retirement age, the demand for fixed income investments that offer stable returns and lower risk compared to equities is increasing. Retirees and near-retirees often prioritize capital preservation and income generation, which fixed income products are well-suited to provide. This demographic trend is particularly prominent in developed countries but is also becoming more relevant in emerging markets as their populations age and accumulate wealth.

Another crucial growth driver is the rising interest rate environment. As central banks around the world shift towards tightening monetary policies to combat inflation, interest rates are gradually increasing. Higher interest rates make newly issued bonds more attractive to investors due to their higher yields. This situation creates opportunities for fixed income asset managers to attract new investments and cater to clients looking for better returns in a higher interest rate environment. Additionally, higher yields can enhance the overall performance of fixed income portfolios, making them more appealing to both institutional and retail investors.

The increasing complexity and diversity of fixed income products is also contributing to market growth. The fixed income market has evolved to include a wide range of instruments beyond traditional government and corporate bonds. Products such as mortgage-backed securities, municipal bonds, and various structured financial instruments offer different risk-return profiles and investment opportunities. This diversification allows asset managers to tailor portfolios to meet specific client needs and preferences, thereby attracting a broader investor base. The development of innovative fixed income products continues to drive growth in this market by expanding the range of investment options available.

In the realm of private equity, the PE Fund Management Fee plays a crucial role in shaping the investment landscape. These fees are typically charged by fund managers to cover the operational costs of managing the fund, including research, administration, and portfolio management. The structure of these fees can vary, often comprising a management fee based on the committed capital and a performance fee tied to the fund's returns. Understanding the intricacies of these fees is essential for investors, as they can significantly impact the net returns on their investments. As private equity continues to grow as an asset class, the transparency and justification of management fees are becoming increasingly important to investors seeking to maximize their returns while ensuring alignment of interests with fund managers.

From a regional perspective, North America remains the largest market for fixed income asset management, driven by the presence of a well-established financial industry, a large pool of institutional investors, and a high level of individual wealth. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. Rapid economic growth, increasing financial literacy, and a burgeoning middle class are driving demand for fixed income investments in countries such as China and India. Additionally, regulatory reforms aimed at developing local bond markets and attracting foreign investment are further propelling the market in this region.

Asset Type Analysis

The fixed income asset management market can be categorized by asset type into government bonds, corporate bonds, municipal bonds, mortgage-backed securities, and others. Each of these asset types offers unique characteristics and appeals to different segments of investors, contributing to the overall growth and diversification of the market.

Government bonds are one of the most significant segments in the fixed income market. Issued by national governments, these bonds are considered low-risk investments due to the backing of the issuing g

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

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