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 rose to 4.76% on December 2, 2025, marking a 0.02 percentage points increase from the previous session. Over the past month, the yield has edged up by 0.06 points and is 0.35 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 December 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.

Index Time Series for BlackRock High Yield Muni Income Bond ETF. The frequency of the observation is daily. Moving average series are also typically included. Under normal circumstances, the fund seeks to achieve its objectives by investing at least 80% of its assets in municipal bonds. Generally, the fund will invest in distressed securities when fund management believes they offer significant potential for higher returns or can be exchanged for other securities that offer this potential. It is non-diversified.

Index Time Series for VanEck Short High Yield Muni ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund normally invests at least 80% of its total assets in securities that comprise the benchmark index. The index is composed of publicly traded municipal bonds that cover the U.S. dollar denominated high yield short-term tax-exempt 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.

Blackrock MuniHoldings Quality Fund (MUE) Forecast: Navigating 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

Diluted-Average-Shares Time Series for MarketAxess Holdings Inc. MarketAxess Holdings Inc., together with its subsidiaries, operates an electronic trading platform for institutional investor and broker-dealer firms in the United States, the United Kingdom, and internationally. The company offers trading technology to access liquidity on its platforms in U.S. high-grade bonds, U.S. high-yield bonds, emerging market debt, eurobonds, municipal bonds, U.S. government bonds, and other fixed-income securities; and executes bond trades between and among institutional investor and broker-dealer clients in an all-to-all anonymous trading environment for corporate bonds through its Open Trading protocols. It also provides automated and algorithmic trading solutions, such as X-Pro, a trading platform to combine trading protocols with its proprietary data and pre-trade analytics; and integrated and actionable data offerings, including CP+ and Axess All, a real-time pricing engine, including Auto-X and portfolio trading. In addition, the company offers various pre-and post-trade services, such as processing, trade matching, trade publication, regulatory transaction reporting, and market and reference data across a range of fixed-income and other products. MarketAxess Holdings Inc. was incorporated in 2000 and is headquartered in New York, New York.

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.

CMU MFS Municipal Income Trust 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

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.

PNF PIMCO New York Municipal Income Fund 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

Zero-Coupon Bonds Market Outlook

According to our latest research, the global zero-coupon bonds market size reached USD 1.32 trillion in 2024, registering a robust performance across developed and emerging economies. The market is poised to expand at a CAGR of 6.8% during the forecast period, with the total market value projected to reach USD 2.43 trillion by 2033. The primary growth driver for the zero-coupon bonds market is the increasing demand for fixed-income securities that offer predictable returns and lower reinvestment risk, especially in volatile interest rate environments. As per our analysis, investor appetite for zero-coupon bonds continues to rise due to their unique characteristics, including deep discount pricing and the absence of periodic interest payments, making them particularly attractive for long-term portfolio strategies.

The growth of the zero-coupon bonds market is significantly influenced by macroeconomic factors such as fluctuating interest rates, inflationary pressures, and the evolving global financial landscape. With central banks in major economies adopting diverse monetary policies to manage economic recovery and inflation, investors are increasingly seeking instruments that provide certainty of returns. Zero-coupon bonds, which pay no periodic interest but are issued at a substantial discount to face value, have gained traction among both institutional and retail investors. These bonds are particularly favored during periods of declining interest rates, as their value tends to appreciate more than traditional coupon-bearing bonds, offering superior capital appreciation potential for investors with long-term horizons.

Another pivotal factor propelling the expansion of the zero-coupon bonds market is the growing need for efficient asset-liability management among pension funds, insurance companies, and sovereign wealth funds. These institutional investors often require instruments with predictable cash flows to match their long-term liabilities, and zero-coupon bonds fit this requirement exceptionally well. Additionally, tax considerations play a crucial role, as the accretion of interest in zero-coupon bonds can be managed more efficiently in certain jurisdictions, providing further incentives for their inclusion in diversified portfolios. The increasing sophistication of financial markets, coupled with the proliferation of innovative debt instruments, is also driving issuers to offer more zero-coupon bonds to meet evolving investor demands.

Technological advancements and digital transformation in the financial services sector are further reshaping the zero-coupon bonds market landscape. The rise of online trading platforms and digital brokerage services has democratized access to fixed-income securities, enabling a broader base of retail investors to participate in the market. Enhanced transparency, real-time pricing, and seamless transaction capabilities have reduced barriers to entry and increased market liquidity. Furthermore, regulatory reforms aimed at improving market efficiency and investor protection have fostered greater confidence in zero-coupon bond investments. As financial literacy improves and investors become more aware of the benefits and risks associated with zero-coupon bonds, the market is expected to witness sustained growth across all major regions.

From a regional perspective, North America currently dominates the global zero-coupon bonds market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, has a well-established market for both government and corporate zero-coupon bonds, supported by a mature financial infrastructure and a diverse investor base. In Europe, regulatory harmonization and the growing demand for alternative fixed-income products are driving market expansion, while Asia Pacific is emerging as a high-growth region due to increasing financial market development and rising institutional investment. Latin America and the Middle East & Africa, though smaller in terms of market size, are expected to register above-average growth rates over the forecast period, fueled by ongoing economic reforms and efforts to deepen domestic capital markets.

Type Analysis

The zero-coupon bonds market can be segmented by type into government zero-coupon bonds, corporate zero-coupon bonds, municipal zero-coupon bonds, and others. Government zero-coupon bonds represent the largest segment, driven by the s

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.

NBO Neuberger Berman New York Municipal 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

Graph and download economic data for ICE BofA AAA US Corporate Index Effective Yield (BAMLC0A1CAAAEY) from 1996-12-31 to 2025-12-01 about AAA, 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.

Nuveen (NMI): Where Municipal Income Meets Investor Goals?

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.

NZF Nuveen Municipal Credit Income Fund

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 Nuveen California Quality Municipal Income Fund

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