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.

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.

Municipal Bonds Market Outlook

According to our latest research, the global municipal bonds market size reached USD 4.2 trillion in 2024, with a recorded compound annual growth rate (CAGR) of 3.8% over the past five years. The market is projected to expand steadily, reaching approximately USD 5.8 trillion by 2033, as per the calculated CAGR. This growth is primarily driven by increasing infrastructure development, rising demand for stable and tax-advantaged investment options, and governments’ ongoing need for capital to fund public projects. The municipal bonds market continues to attract both individual and institutional investors due to its relative stability and attractive risk-adjusted returns compared to other fixed-income securities.

One of the main growth factors for the municipal bonds market is the persistent need for infrastructure development and modernization across both developed and emerging economies. Governments worldwide are increasingly relying on municipal bonds to finance projects such as transportation networks, water and sewage systems, schools, and hospitals. As fiscal constraints tighten and traditional funding sources become less accessible, municipal bonds offer state and local governments a viable avenue to raise capital efficiently. This trend is further reinforced by the growing awareness of the importance of sustainable and resilient infrastructure, driving the issuance of green and social municipal bonds. The proliferation of such bonds not only addresses critical societal needs but also attracts a new segment of environmentally and socially conscious investors, thereby broadening the market base.

Another key driver is the favorable tax treatment that municipal bonds offer, particularly in countries like the United States, where interest income from many municipal bonds is exempt from federal income tax and, in some cases, state and local taxes as well. This tax advantage makes municipal bonds highly attractive for high-net-worth individuals and institutional investors seeking to optimize after-tax returns. Additionally, in a global environment characterized by low interest rates and volatile equity markets, municipal bonds are perceived as a safe haven, offering steady income with relatively low default risk. This perception has led to increased allocations to municipal bonds within diversified investment portfolios, further fueling market growth.

Technological advancements and digitalization have also played a significant role in the expansion of the municipal bonds market. The advent of online trading platforms and digital distribution channels has democratized access to municipal bonds, enabling a broader spectrum of investors to participate. These platforms facilitate transparency, improve price discovery, and reduce transaction costs, making it easier for both novice and seasoned investors to buy and sell municipal bonds. Furthermore, regulatory reforms aimed at enhancing market transparency and investor protection have bolstered confidence in municipal bonds as a reliable investment class. As a result, market participation has widened, contributing to increased liquidity and overall market growth.

From a regional perspective, North America, particularly the United States, continues to dominate the municipal bonds market, accounting for the largest share of issuances and outstanding value. However, Europe and Asia Pacific are emerging as significant growth regions, driven by increasing urbanization, infrastructure spending, and the adoption of innovative financing mechanisms. In Europe, the push for sustainable finance and green investments is fostering the development of new municipal bond structures, while in Asia Pacific, rapid economic growth and urban development are prompting governments to explore municipal bonds as a means of funding critical projects. Latin America and the Middle East & Africa, though smaller in market size, are also witnessing gradual adoption of municipal bonds, supported by regulatory reforms and growing investor interest.

Type Analysis

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

Revenue Bonds Market Outlook

According to our latest research, the global revenue bonds market size reached USD 2.57 trillion in 2024, underscoring its critical role in public finance and infrastructure development worldwide. The market is projected to grow at a robust CAGR of 6.1% from 2025 to 2033, indicating strong investor interest and increasing demand for alternative financing mechanisms among public sector entities. By 2033, the market size is expected to reach approximately USD 4.37 trillion. This growth is primarily driven by escalating infrastructure investments, governmental fiscal constraints, and the ongoing need for modernization and expansion across sectors such as transportation, utilities, and education.

One of the key growth factors propelling the revenue bonds market is the persistent global demand for infrastructure development and upgrades. Governments worldwide are under mounting pressure to revitalize aging infrastructure, from roads and bridges to water treatment facilities and public transportation systems. However, fiscal constraints and budgetary limitations often hinder direct public funding for these projects. Revenue bonds offer a viable solution by allowing municipalities and other public entities to finance essential projects through future income streams generated by the projects themselves, such as tolls, utility payments, or lease revenues. This self-sustaining financing model not only mitigates risks for taxpayers but also attracts a diverse pool of investors seeking stable, long-term returns.

Another significant driver is the increasing sophistication and diversification of the revenue bonds market, with new structures and hybrid instruments emerging to meet the evolving needs of issuers and investors alike. Innovations such as green revenue bonds and public-private partnership (PPP) models are gaining traction, especially as sustainability and ESG (Environmental, Social, and Governance) criteria become central to investment decisions. These developments are further supported by favorable regulatory frameworks in several regions, which streamline the issuance process and enhance transparency. As a result, both institutional and retail investors are increasingly participating in the market, attracted by the relatively lower risk profile and predictable cash flows associated with revenue bonds compared to general obligation bonds.

Additionally, the global interest rate environment is influencing market dynamics. With central banks in major economies maintaining relatively low interest rates, investors are seeking fixed-income securities that offer attractive yields without excessive risk. Revenue bonds, backed by dedicated revenue streams rather than general taxation, present an appealing option in this context. The market is also benefiting from technological advancements in bond issuance and management, including digital platforms that facilitate greater efficiency, transparency, and accessibility for both issuers and investors. This technological progress is expected to further accelerate market expansion over the forecast period.

Regionally, North America continues to dominate the revenue bonds market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, remains the epicenter of activity, driven by an extensive municipal bond market and a strong tradition of leveraging revenue bonds for infrastructure financing. Meanwhile, emerging economies in Asia Pacific and Latin America are experiencing rapid growth, fueled by urbanization and increased public investment in critical sectors. Europe’s market is characterized by innovation in green and sustainability-linked revenue bonds, reflecting the region’s commitment to environmental objectives. Each region exhibits unique market dynamics shaped by regulatory frameworks, investor preferences, and economic priorities.

Type Analysis

The revenue bonds market is segmented by type into General Revenue Bonds, Limited Tax Revenue Bonds, Special Tax Revenue Bonds, Lease Revenue Bonds, and Others, each serving distinct financing needs and risk profiles. General Revenue Bonds are the most prevalent, backed by the overall revenues of the issuing entity, such as utility fees or transportation fares. These bonds are favored for their flexibility and broad revenue base, making them attractive to risk-averse investors seeking stable returns. The market for general revenue bonds is particularly ro

Municipal bond markets experienced a significant amount of strain in response to the COVID-19 crisis, creating liquidity and credit concerns among market participants. During the economic shutdown resulting from the pandemic, income tax revenues were deferred and sales tax revenues decreased beginning in spring 2020, while the cost of borrowing significantly increased for municipal issuers. To aid municipal borrowing needs, the Federal Reserve implemented the Municipal Liquidity Facility (MLF) on April 9, 2020. In this analysis we describe the municipal market conditions as they evolved during 2020, we document the response by the Federal Reserve to municipal market distress with a focus on the MLF, and we conduct an event study to examine MLF-related impacts on market index yield spreads. We detail two case studies that compare yield spreads for two issuers that had sold debt to the MLF and find that yield spreads in secondary market transactions for these two issuers were notably reduced after a public announcement of intent to sell debt to the MLF. Our results present additional evidence that the MLF had a positive impact on municipal market functioning during the pandemic period.

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.

PYN PIMCO New York Municipal Income Fund III 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

High Yield Green Bonds Market Outlook

As per our latest research, the global High Yield Green Bonds market size reached USD 146.2 billion in 2024, driven by robust investor appetite for sustainable finance and increasing regulatory support for green initiatives. The market is experiencing a strong upward trajectory, registering a CAGR of 12.5% from 2025 to 2033. By 2033, the High Yield Green Bonds market is forecasted to attain a value of USD 418.6 billion. This impressive growth is primarily attributed to the escalating demand for climate-resilient infrastructure, the proliferation of ESG (Environmental, Social, and Governance) investment mandates, and heightened awareness among issuers and investors regarding the environmental impact of capital allocation.

One of the most significant growth factors in the High Yield Green Bonds market is the increasing alignment of global financial policies with the Paris Agreement and the United Nations Sustainable Development Goals (SDGs). Governments and financial regulators worldwide are actively encouraging the issuance of green bonds by introducing favorable policies, tax incentives, and sustainability-linked frameworks. This has resulted in a surge in both the number and diversity of issuers entering the market. Corporates, sovereigns, and municipalities are leveraging high yield green bonds to finance renewable energy projects, energy efficiency upgrades, and sustainable infrastructure, thereby driving market expansion. The growing emphasis on green finance is also fostering innovation in bond structures and reporting mechanisms, further enhancing investor confidence and participation.

Another pivotal driver is the evolving investor landscape, with institutional investors, asset managers, and pension funds increasingly integrating ESG criteria into their portfolios. The appetite for high yield green bonds is particularly notable among investors seeking both attractive returns and positive environmental impact. The diversification benefits offered by green bonds, coupled with their relatively lower default rates compared to traditional high yield instruments, are making them a preferred choice for risk-adjusted returns. Moreover, the proliferation of green bond indices and dedicated green bond funds is facilitating greater market access and liquidity, enabling a broader range of investors to participate in the sustainable finance movement.

Technological advancements and data transparency are further catalyzing growth in the High Yield Green Bonds market. The adoption of blockchain and digital platforms for green bond issuance, trading, and impact reporting is streamlining processes and enhancing traceability. This technological integration is not only reducing transaction costs but also improving the credibility and accountability of green bond projects. Enhanced transparency in the use of proceeds, third-party verification, and real-time impact measurement are addressing investor concerns around greenwashing, thereby building trust and accelerating market growth. As the market matures, the standardization of green bond frameworks and reporting practices is expected to further solidify its position as a mainstream sustainable finance instrument.

From a regional perspective, Europe continues to dominate the High Yield Green Bonds market, accounting for over 39% of global issuance in 2024, followed closely by North America and Asia Pacific. The European Union’s Green Deal and the introduction of the EU Taxonomy for sustainable activities have provided a robust regulatory backdrop, spurring significant issuance activity across corporate, sovereign, and municipal segments. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by government-backed green infrastructure programs, particularly in China, Japan, and South Korea. North America, led by the United States and Canada, is witnessing increased participation from both public and private sector issuers, driven by ambitious climate targets and rising investor demand for sustainable assets. The Middle East & Africa and Latin America, while currently smaller contributors, are expected to register accelerated growth rates as green finance frameworks and climate resilience initiatives gain traction in these regions.

Bond Type Analysis

The Bond Type segment within the High Yield Green Bonds market is categorized into Corporate, Sovereign, Municipal, and Others

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

Fixed Income Assets Management Market Outlook

The global fixed income assets management market size is projected to grow significantly from USD 10 trillion in 2023 to USD 15.5 trillion by 2032, at a compound annual growth rate (CAGR) of 5.2%. This growth is driven by a combination of factors including the increasing demand for stable and predictable income streams, the aging population in developed economies, and the global shift towards more conservative investment strategies. The market is also influenced by regulatory changes and technological advancements that enhance the accessibility and management of fixed income assets.

A major growth factor in the fixed income assets management market is the rising demand for stable income sources, especially in uncertain economic climates. Investors, both institutional and retail, are increasingly seeking investment vehicles that offer predictable returns with lower risk profiles compared to equities. This is particularly appealing in times of economic volatility and low interest rates, where traditional savings accounts and other low-risk options provide minimal returns. Fixed income assets such as government and corporate bonds are particularly attractive for their ability to provide steady income through regular interest payments.

Another significant driver is the demographic shift in developed economies towards an aging population. As people approach retirement, their investment strategies often shift from growth-oriented assets to more conservative, income-generating investments. Fixed income assets match this need perfectly, offering lower volatility and preserving capital while generating a steady stream of income. This demographic trend is expected to sustain and potentially increase demand for fixed income management services, as retirees seek to secure their financial future through reliable investments.

Technological advancements are also playing a crucial role in the growth of the fixed income assets management market. Innovations such as robo-advisors and advanced analytics tools are making it easier for investors to access and manage their fixed income portfolios. These technologies facilitate better decision-making by providing real-time data, risk assessment, and performance tracking. Additionally, the proliferation of online platforms and financial applications has democratized access to fixed income investments, allowing even small retail investors to participate in markets that were traditionally dominated by large institutional players.

From a regional perspective, North America continues to dominate the fixed income assets management market owing to its well-established financial infrastructure and a high concentration of institutional investors. However, Asia Pacific is emerging as a significant growth region due to its expanding middle class and increasing investor awareness. Europe also shows strong potential, driven by regulatory support and a growing preference for sustainable and ethical investments. Latin America and the Middle East & Africa are gradually catching up, albeit at a slower pace, due to evolving financial markets and increasing foreign investments.

Asset Type Analysis

In the realm of fixed income assets management, government bonds hold a prominent position due to their low risk and high liquidity. Governments issue these bonds to finance public projects and manage national debt, making them a relatively safe investment. Institutional investors, such as pension funds and insurance companies, favor government bonds for their stability and predictable returns. Retail investors also appreciate the security offered by these bonds, especially in uncertain economic times. The demand for government bonds is expected to remain strong, driven by ongoing government borrowing and the need for risk-averse investment options.

Corporate bonds are another significant segment within the fixed income assets market. These bonds are issued by corporations to finance operations, expansions, or other business activities. While they carry higher risk compared to government bonds, they also offer higher returns, attracting investors seeking a balance between risk and reward. The corporate bond market is diverse, ranging from investment-grade bonds issued by financially strong companies to high-yield bonds from riskier issuers. The ongoing growth in corporate activities and the need for capital are expected to sustain the demand for corporate bonds.

Municipal bonds, issued by local governments or municipalities, are popular for their tax advantages. Interest

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

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

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.

NBH Neuberger Berman 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

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

Bond Futures Market Outlook

The global Bond Futures Market achieved a market size of USD 24.3 billion in 2024, as per our latest research, exhibiting robust momentum driven by increasing demand for risk management tools and rising participation from institutional investors. The market is projected to expand at a CAGR of 7.1% during the forecast period, reaching an estimated USD 45.1 billion by 2033. This growth trajectory is underpinned by evolving financial regulations, the adoption of advanced trading technologies, and the ongoing globalization of capital markets, which collectively enhance liquidity and transparency within the bond futures ecosystem.

One of the primary growth factors for the Bond Futures Market is the increasing need for effective risk management solutions among institutional investors, banks, and asset managers. As global interest rate volatility intensifies, market participants are increasingly turning to bond futures as a strategic instrument for hedging interest rate risk and managing portfolio exposures. The ability to lock in yields and mitigate adverse price movements has made bond futures a preferred choice, particularly amid uncertain macroeconomic conditions and fluctuating monetary policies. Additionally, the growing sophistication of financial products and the integration of bond futures into multi-asset strategies further amplify their relevance in modern portfolio management.

Technological advancements have also played a pivotal role in the expansion of the Bond Futures Market. The proliferation of electronic trading platforms and algorithmic trading systems has significantly enhanced market efficiency, reduced transaction costs, and broadened access for a diverse set of market participants. This democratization of trading infrastructure, coupled with real-time data analytics and automated execution, has enabled both institutional and individual investors to participate in bond futures markets with greater confidence and agility. Moreover, the evolution of regulatory frameworks emphasizing transparency and standardized contracts has fostered a more robust and resilient bond futures ecosystem, encouraging further adoption across global markets.

Another critical driver is the globalization of financial markets, which has facilitated cross-border capital flows and expanded the investor base for bond futures. As emerging economies deepen their bond markets and integrate with global financial systems, the demand for standardized, exchange-traded derivatives such as bond futures continues to rise. This trend is particularly evident in regions like Asia Pacific and Latin America, where regulatory reforms and infrastructure enhancements are accelerating market development. Additionally, the increasing use of bond futures for speculative and arbitrage purposes adds a new dimension to market liquidity, fostering greater price discovery and efficient risk transfer mechanisms.

From a regional perspective, North America has maintained its dominance in the Bond Futures Market, accounting for the largest share in 2024, driven by the presence of established exchanges, sophisticated investors, and a mature regulatory environment. Europe follows closely, benefiting from the integration of financial markets and the adoption of advanced trading technologies. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid economic expansion, regulatory modernization, and the proliferation of electronic trading platforms. Other regions, including Latin America and the Middle East & Africa, are also witnessing increased adoption, albeit at a more gradual pace, as they work to strengthen market infrastructure and regulatory oversight.

Contract Type Analysis

The Contract Type segment in the Bond Futures Market is broadly categorized into Government Bond Futures, Corporate Bond Futures, Municipal Bond Futures, and Others. Government Bond Futures represent the largest share of the market, as they offer high liquidity, standardized contracts, and are often used as benchmarks for interest rate movements. These contracts are particularly favored by institutional investors and central banks for hedging and managing sovereign debt exposures. The dominance of government bond futures is further supported by the deep and liquid underlying government bond markets, which provide a solid foundation for active trading and efficient price discovery.