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.

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 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.

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.

This is a dataset of General Obligation, Dormitory Authority of the State of New York, and the New York City Municipal Water Finance Authority Interest Rate Exchange Agreements. This data set provides information on outstanding New York city bonds, interest rate exchange agreements, and projected debt service on those bonds. Note: In the Table "General Obligation, Dormitory Authority of the State of New York, and the New York City Municipal Water Finance Authority Interest Rate Exchange Agreements",*Mark-to-Market Values are calculated by a third party swap adviser, Mohanty Gargiulo LLC.

Graph and download economic data for Bond Buyer Go 20-Bond Municipal Bond Index (DISCONTINUED) 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.

This dataset contains the source information for pumping rates for municipal and industrial (MnI) wells in New Mexico within the Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM). In RGTIHM, these wells are considered the Other New Mexico Extra (ONMXA) group.

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.

Forecast: Municipal Waste Recycling Rate in France 2024 - 2028 Discover more data with ReportLinker!

Some 2.7 billion people - or 25 percent of the global population - did not have access to waste collection services in 2020. Collection rates were the lowest in Central and South Asia, and Sub-Saharan Africa, where they stood below 40 percent. In contrast, roughly the entire population in North America and Western Europe had access to such services.

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.

Putnam Municipal Opportunities (PMO) - Navigating the Municipal 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 dataset is about stocks per day and is filtered where the stock is MUNI and the date is after 2024-09-14, featuring 3 columns: date, lowest price, and stock. The preview is ordered by date (descending).

Numerous OECD countries have dramatically improved their municipal recovery rates over the past decade. As of 2021, Slovenia had the highest recycling rate, at 76.6 percent - up from less than 30 percent in 2010. Turkey has also seen recovery rates rise in the period, surpassing 13 percent in 2020. Meanwhile in Denmark, municipal waste recovery dropped from roughly 50 percent to some 33 percent in 2021.

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.

MUI BlackRock Municipal Income 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

In 2023, the recycling rate of municipal waste in the European Union (EU-27) was estimated at 48.2 percent. This was a slight decline from the previous year, when figures reached 49.1 percent. The recycling rate of municipal waste in the European Union has almost doubled since the turn of the century.

In 2023, almost 78 percent of the municipal waste collected in Veneto was sorted — this was more than in any other region across Italy that year. Meanwhile, the share of waste subject to sorted collection in Sicily stood below 52 percent of the total municipal waste.

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.

NUV Nuveen Municipal Value 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

In 2022, the recycling rate of municipal waste in the United Kingdom stood at 42.5 percent, when considering recycling and composting combined, slightly down from the previous year. Municipal waste recycling rates in the UK nearly quadrupled between 2000 and 2019, peaking at 44.3 percent in the later year. With progress stalling in recent years, the UK is off-track to reach its target to recycle 55 percent of municipal waste by 2025, and 60 percent by 2030.

Forecast: Municipal Waste Recycling Rate in Sweden 2024 - 2028 Discover more data with ReportLinker!