An important indicator of the financial strength of governmental entity is its bond rating. The bond rating is similar in nature to the credit score of an individual – the higher the score, the better the ability to borrow money to finance purchases at a lower interest rate. Similarly, the higher the bond rating for a governmental entity, the more opportunities to borrow money for capital needs at lower interest rates. A high bond rating is in excellent indicator of the overall financial health of a government.This measure is obtained each year when the city seeks to issue bonds to finance its’ projects. As part of this process, bond ratings are always obtained from the rating agencies: Standard & Poor’s. Fitch Ratings and Moody's Investor Service.This page provides data for the Bond Rating performance measure.Bond ratings are a reflection of the financial strength of an entity. A high rating means an entity can issue bonds to finance capital projects at lower interest rates; lower rates result in less interest to be paid on the repayment of the bonds. Ultimately, this lowers the costs of our capital projects to our taxpayers.The performance measure dashboard is available at 5.04 Bond Rating.Additional InformationSource: Standard & Poors, Moody's Investor Service, and Fitch Ratings are the major bond rating agencies in the United States and are widely used by governmental and non-governmental entities throughout the country.Contact: Jerry HartContact E-Mail: Jerry_Hart@tempe.govData Source Type: ExcelPreparation Method: ManualPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

The 50th percentile credit score among credit card accounts. The current credit score is the most recently determined commercially available credit score of the primary account holder. If an updated commercial credit bureau score is not available or is not currently being used by the reporting institution to evaluate the primary account holder's creditworthiness, the institution is instructed to map the most current internal credit score used to evaluate the primary account holder's creditworthiness to a commercially available credit bureau score. The source of the current credit score may vary by FR Y-14M reporting firm and even within the firm's reporting. Only credit card accounts with current credit scores between 150 and 950 are included in the current credit score percentile calculations. For more detail see: methodology (https://www.philadelphiafed.org/-/media/frbp/assets/surveys-and-data/y14/y-14-data-methodology).

Credit report of Pakistan Credit Rating Agency contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

The 25th percentile credit score among credit card accounts. The current credit score is the most recently determined commercially available credit score of the primary account holder. If an updated commercial credit bureau score is not available or is not currently being used by the reporting institution to evaluate the primary account holder's creditworthiness, the institution is instructed to map the most current internal credit score used to evaluate the primary account holder's creditworthiness to a commercially available credit bureau score. The source of the current credit score may vary by FR Y-14M reporting firm and even within the firm's reporting. Only credit card accounts with current credit scores between 150 and 950 are included in the current credit score percentile calculations. For more detail see: methodology (https://www.philadelphiafed.org/-/media/frbp/assets/surveys-and-data/y14/y-14-data-methodology).

The average credit score of Americans - as measured by the FICO score - increased for the first time in about two years in early 2023. The average score in April 2024 stood at ***. The score as displayed ranges from *** to *** and is based on three different consumer reporting agencies (CRAs) in the United States, namely Equifax, TransUnion, and Experian. The source adds that the score was especially impacted by slowing inflation, lower unemployment figures and changes to certain consumer credit data.

Village (Neighborhood) Boundaries across the Nation

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.

Can Cavendish Financial (CAV) Chart a Course for 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

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.

Direct Line's (DLG) Future: A Road Map of Growth or a Skidding Stop?

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 data set is up-to-date as of 2016, but is no longer actively maintained by the City of South Bend.

The Water Resources Agency's disaster emergency response team of the Ministry of Economic Affairs further combines real-time data such as rainfall, water level, and reservoir information with long-term disaster response experience and computer technology to provide reservoir alerts for the public and relevant units. This helps the public understand the risk of home flooding, prepare early, and reduce the occurrence of disasters. This dataset is linked to a Keyhole Markup Language (KML) file list. This format is a markup language based on the XML (eXtensible Markup Language) syntax standard, developed and maintained by Keyhole, a subsidiary of Google, to express geographic annotations. Documents written in the KML language are KML files, which use the XML file format and are used in Google Earth related software (Google Earth, Google Map, Google Maps for mobile...) to display geographic data (including points, lines, polygons, polyhedra, and models...). Many GIS-related systems now also use this format for the exchange of geographic data, and the fields and codes of this data are all in UTF-8.

Graph and download economic data for ICE BofA BBB US Corporate Index Effective Yield (BAMLC0A4CBBBEY) from 1996-12-31 to 2025-07-02 about BBB, yield, corporate, interest rate, interest, rate, and USA.

View the spread between a computed option-adjusted index of all BBB-rated bonds and a spot Treasury curve.

This data set is up-to-date as of 2014, but is no longer actively maintained by the City of South Bend.