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 4.96% on July 11, 2025, marking a 0.09 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.11 points and is 0.56 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.

Historical dividend payout and yield for SPDR Nuveen Bloomberg Municipal Bond ETF (TFI) since 2009. The current TTM dividend payout for SPDR Nuveen Bloomberg Municipal Bond ETF (TFI) as of June 30, 2025 is $1.40. The current dividend yield for SPDR Nuveen Bloomberg Municipal Bond ETF as of June 30, 2025 is 3.16%.

Historical dividend payout and yield for BNY Mellon Strategic Municipal Bond Fund (DSM) since 1992. The current TTM dividend payout for BNY Mellon Strategic Municipal Bond Fund (DSM) as of June 11, 2025 is $0.18. The current dividend yield for BNY Mellon Strategic Municipal Bond Fund as of June 11, 2025 is 3.27%.

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.

National Muni Bond ETF: A True Haven?

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.

Virginia Muni-Bond Paydays Promising For NPV?

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

Snapshot img

Fixed Income Assets Management Market Size 2025-2029

The fixed income assets management market size is forecast to increase by USD 9.16 tr at a CAGR of 6.3% between 2024 and 2029.

The market is experiencing significant growth, driven by increasing investor interest in fixed income securities as a hedge against market volatility. A key trend in this market is the expansion of bond Exchange-Traded Funds (ETFs), which offer investors liquidity, diversification, and cost savings. However, this market is not without risks. Transactions in fixed income assets involve complexities such as credit risk, interest rate risk, and liquidity risk, which require sophisticated risk management strategies. As global investors seek to capitalize on market opportunities and navigate these challenges effectively, they must stay informed of regulatory changes, market trends, and technological advancements. Companies that can provide innovative solutions for managing fixed income risks and optimizing returns will be well-positioned to succeed in this dynamic market.

What will be the Size of the Fixed Income Assets Management Market during the forecast period?

Request Free SampleThe fixed income assets market in the United States continues to be an essential component of investment portfolios for various official institutions and individual investors. With an expansive market size and growth, fixed income securities encompass various debt instruments, including corporate bonds and government treasuries. Interest rate fluctuations significantly impact this market, influencing investment decisions and affecting the returns from interest payments on these securities. Fixed income Exchange-Traded Funds (ETFs) and index managers have gained popularity due to their cost-effective and diversified investment options. However, the credit market volatility and associated default risk pose challenges for investors. In pursuit of financial goals, investors often choose fixed income funds over equities for their stable dividend income and tax savings benefits. Market risk and investors' risk tolerance are crucial factors in managing fixed income assets. Economic uncertainty and interest rate fluctuations necessitate active management by asset managers, hedge funds, and mutual funds. The fund maturity and investors' financial goals influence the choice between various fixed income securities, such as treasuries and loans. Despite the challenges, the market's direction remains positive, driven by the continuous demand for income-generating investments.

How is this Fixed Income Assets Management Industry segmented?

The fixed income assets management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD tr' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeCoreAlternativeEnd-userEnterprisesIndividualsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaSouth AmericaMiddle East and Africa

By Type Insights

The core segment is estimated to witness significant growth during the forecast period.The fixed income asset management market encompasses a diverse range of investment vehicles, including index investing, pension funds, official institutions, mutual funds, investment advisory services, and hedge funds. This asset class caters to income holders with varying risk tolerances, offering securities such as municipal bonds, government bonds, and high yield bonds through asset management firms. Institutional investors, insurance companies, and corporations also play significant roles in this sector. Fixed income securities, including Treasuries, municipal bonds, corporate bonds, and debt securities, provide regular interest payments and can offer tax savings, making them attractive for investors with financial goals. However, liquidity issues and credit market volatility can pose challenges. The Federal Reserve's interest rate decisions and economic uncertainty also impact the fixed income market. Asset management firms employ various strategies, such as the core fixed income (CFI) strategy, which invests in a mix of investment-grade fixed-income securities. CFI strategies aim to deliver consistent performance by carefully managing portfolios, considering issuer creditworthiness, maturity, and jurisdiction. Fixed income funds, including government bonds and corporate bonds, offer lower market risk compared to equities. Investors can choose from various investment vehicles, including mutual funds, ETFs, and index funds managed by active managers or index managers. Fixed income ETFs, in particular, provide investors with the benefits of ETFs, such as liquidity and transparency, while offering exposure to the fixed income market. Despite market risks and liquidity issues, the fixed income asset management market continues to be

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

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.

Blackrock MuniHoldings Quality Fund II (MUE) Stock: A Solid Bet on Municipal Bond Growth

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

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 1003 companies listed on the Euronext Amsterdam (XAMS) in Netherlands. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Netherlands:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Netherlands:

Amsterdam Stock Exchange (AEX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Amsterdam Stock Exchange. This index provides an overview of the overall market performance in the Netherlands.

Amsterdam Stock Exchange (AEX) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Amsterdam Stock Exchange. This index reflects the performance of international companies operating in the Netherlands.

Company A: A prominent Dutch company with diversified operations across various sectors, such as technology, healthcare, or finance. This company's stock is widely traded on the Amsterdam Stock Exchange.

Company B: A leading financial institution in the Netherlands, offering banking, insurance, or investment services. This company's stock is actively traded on the Amsterdam Stock Exchange.

Company C: A major player in the Dutch energy or consumer goods sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Amsterdam Stock Exchange.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Netherlands, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Netherlands ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Netherlands?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Netherlands exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment method...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 214 companies listed on the Panama Stock Exchange (XPTY) in Panama. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Panama:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Panama:

Panamanian Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Panamanian Stock Exchange (Bolsa de Valores de Panamá). This index provides an overview of the overall market performance in Panama.

Panamanian Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Panamanian Stock Exchange. This index reflects the performance of international companies operating in Panama.

Company A: A prominent Panamanian company with diversified operations across various sectors, such as shipping, logistics, or finance. This company's stock is widely traded on the Panamanian Stock Exchange.

Company B: A leading financial institution in Panama, offering banking, insurance, or investment services. This company's stock is actively traded on the Panamanian Stock Exchange.

Company C: A major player in the Panamanian energy or real estate sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Panamanian Stock Exchange.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Panama, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Panama ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Panama?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Panama exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direc...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2113 companies listed on the Libyan Stock Market (XLSM) in Libya. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Libya:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Libya:

Libyan Stock Market Index: The main index that tracks the performance of domestic companies listed on the Libyan Stock Market. This index provides an overview of the overall market performance in Libya.

Foreign Company Index: The index that tracks the performance of foreign companies listed on the Libyan Stock Market. This index reflects the performance of international companies operating in Libya.

Company A: A prominent Libyan company operating in various sectors, such as telecommunications, energy, or banking. This company's stock is widely traded on the Libyan Stock Market.

Company B: A leading financial services provider in Libya, offering banking, insurance, or investment services. This company's stock is actively traded on the Libyan Stock Market.

Company C: A major player in the Libyan agricultural sector, involved in the production and distribution of agricultural products. This company's stock is listed and actively traded on the Libyan Stock Market.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Libya, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Libya ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Libya?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Libya exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive the data?

‍Techsalerato...

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.

iBonds ETF: A Municipal Bond Haven in the Horizon?

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

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 14 companies listed on the Dominican Republic Stock Exchange (XBVR) in Dominican Republic. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Dominican Republic:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Dominican Republic:

Dow Jones Dominican Republic Index: The Dow Jones Dominican Republic Index represents the performance of companies listed on the Dominican Republic Stock Exchange (Bolsa de Valores de la República Dominicana). It serves as a benchmark for tracking the overall market performance in the country.

Banco Popular Dominicano: Banco Popular Dominicano is one of the largest banks in the Dominican Republic, offering a range of banking and financial services to individuals and businesses. The securities of Banco Popular Dominicano are actively traded on the Dominican Republic Stock Exchange.

Grupo Financiero BHD León: Grupo Financiero BHD León is a financial group that operates in the Dominican Republic, providing banking, insurance, and financial services. The securities of Grupo Financiero BHD León are listed and traded on the Dominican Republic Stock Exchange.

Banco de Reservas de la República Dominicana: Banco de Reservas, also known as Banreservas, is the state-owned bank of the Dominican Republic. It offers a wide range of banking and financial services to customers. The securities of Banreservas are listed on the Dominican Republic Stock Exchange.

Altice Dominicana: Altice Dominicana is a subsidiary of Altice Group, a multinational telecommunications company. Altice Dominicana provides telecommunication services in the Dominican Republic. The securities of Altice Dominicana are listed and traded on the Dominican Republic Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Dominican Republic, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Dominican Republic ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Dominican Republic?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Dominican Republic exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 16 companies listed on the Malawi Stock Exchange (XMSW) in Malawi. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

Top 5 used data fields in the End-of-Day Pricing Dataset for Malawi:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Malawi:

Malawi Stock Exchange (MSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Malawi Stock Exchange. This index provides an overview of the overall market performance in Malawi.

Malawi Stock Exchange (MSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Malawi Stock Exchange. This index reflects the performance of international companies operating in Malawi.

Company X: A prominent Malawian company with diversified operations across various sectors, such as manufacturing, telecommunications, or energy. This company's stock is widely traded on the Malawi Stock Exchange.

Company Y: A leading financial institution in Malawi, offering banking, insurance, or investment services. This company's stock is actively traded on the Malawi Stock Exchange.

Company Z: A major player in the Malawian agricultural sector, involved in the production and distribution of agricultural products. This company's stock is listed and actively traded on the Malawi Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Malawi, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Malawi ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Malawi?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Malawi exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facili...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 85 companies listed on the Jamaica Stock Exchange (XJAM) in Jamaica. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Jamaica:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Jamaica:

Jamaica Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Jamaica Stock Exchange. This index provides an overview of the overall market performance in Jamaica.

Jamaica Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Jamaica Stock Exchange. This index reflects the performance of international companies operating in Jamaica.

Company A: A prominent Jamaican company with diversified operations across various sectors, such as tourism, agriculture, or finance. This company's stock is widely traded on the Jamaica Stock Exchange.

Company B: A leading financial institution in Jamaica, offering banking, insurance, or investment services. This company's stock is actively traded on the Jamaica Stock Exchange.

Company C: A major player in the Jamaican energy or manufacturing sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Jamaica Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Jamaica, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Jamaica ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Jamaica?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Jamaica exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, fac...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 48 companies listed on the Stock Exchange of Mauritius (XMAU) in Mauritius. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

Top 5 used data fields in the End-of-Day Pricing Dataset for Mauritius:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Mauritius:

Mauritius Stock Exchange (SEM) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Stock Exchange of Mauritius. This index provides an overview of the overall market performance in Mauritius.

Mauritius Stock Exchange (SEM) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Stock Exchange of Mauritius. This index reflects the performance of international companies operating in Mauritius.

Company A: A prominent Mauritian company with diversified operations across various sectors, such as tourism, financial services, or technology. This company's stock is widely traded on the Stock Exchange of Mauritius.

Company B: A leading financial institution in Mauritius, offering banking, insurance, or investment services. This company's stock is actively traded on the Stock Exchange of Mauritius.

Company C: A major player in the Mauritian agricultural sector, involved in the production and export of agricultural products. This company's stock is listed and actively traded on the Stock Exchange of Mauritius.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Mauritius, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Mauritius ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Mauritius?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Mauritius exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, includi...

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