The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

30 Year Mortgage Rate in the United States decreased to 6.63 percent in August 7 from 6.72 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

United States CSI: Expected Interest Rates: Next Yr: Go Down data was reported at 4.000 % in May 2018. This records a decrease from the previous number of 6.000 % for Apr 2018. United States CSI: Expected Interest Rates: Next Yr: Go Down data is updated monthly, averaging 11.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 54.000 % in Jun 1980 and a record low of 3.000 % in May 2014. United States CSI: Expected Interest Rates: Next Yr: Go Down data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

Fixed 30-year mortgage rates in the United States averaged 6.77 percent in the week ending August 1 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

Iran IR: Lending Interest Rate data was reported at 18.000 % pa in 2016. This records an increase from the previous number of 14.210 % pa for 2015. Iran IR: Lending Interest Rate data is updated yearly, averaging 12.000 % pa from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 18.000 % pa in 2016 and a record low of 11.000 % pa in 2013. Iran IR: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Interest Rates. Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; ;

This table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold.
Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and
2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

Croatia HR: Interest Rate Spread data was reported at 7.227 % pa in 2014. This records a decrease from the previous number of 7.726 % pa for 2013. Croatia HR: Interest Rate Spread data is updated yearly, averaging 9.048 % pa from Dec 1993 (Median) to 2014, with 22 observations. The data reached an all-time high of 1,064.296 % pa in 1993 and a record low of 6.320 % pa in 2001. Croatia HR: Interest Rate Spread data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Croatia – Table HR.World Bank.WDI: Interest Rates. Interest rate spread is the interest rate charged by banks on loans to private sector customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability.;International Monetary Fund, International Financial Statistics and data files.;Median;

Analysis of ‘Annual Market Information Indices’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-c410c7a0-14c3-442b-b75f-4c230ec59406 on 13 January 2022.

--- Dataset description provided by original source is as follows ---

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

--- Original source retains full ownership of the source dataset ---

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Average mortgage interest; highest and lowest interest rates Data available: annual figures from 1993, monthly figures from 1994. Discontinued; There will be no more updates. Info service: http://www.cbs.nl/infoservice. Copyright (c) Statistics Netherlands. Reproduction is permitted, provided Statistics Netherlands is cited as the source.

Context

Have you ever wondered how lenders use various factors such as credit score, annual income, the loan amount approved, tenure, debt-to-income ratio etc. and select your interest rates?

The process, defined as ‘risk-based pricing’, uses a sophisticated algorithm that leverages different determining factors of a loan applicant. Selection of significant factors will help develop a prediction algorithm which can estimate loan interest rates based on clients’ information. On one hand, knowing the factors will help consumers and borrowers to increase their credit worthiness and place themselves in a better position to negotiate for getting a lower interest rate. On the other hand, this will help lending companies to get an immediate fixed interest rate estimation based on clients information. Here, your goal is to use a training dataset to predict the loan rate category (1 / 2 / 3) that will be assigned to each loan in our test set.

You can use any combination of the features in the dataset to make your loan rate category predictions. Some features will be easier to use than others.

Acknowledgements

https://datahack.analyticsvidhya.com/contest/janatahack-machine-learning-for-banking/True/#ProblemStatement

Nepal NP: Real Interest Rate data was reported at -6.207 % pa in 2010. This records an increase from the previous number of -6.823 % pa for 2009. Nepal NP: Real Interest Rate data is updated yearly, averaging 3.657 % pa from Dec 1975 (Median) to 2010, with 29 observations. The data reached an all-time high of 18.214 % pa in 1977 and a record low of -12.173 % pa in 1975. Nepal NP: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

Access to finance of small and medium enterprises. Topics: development of the following indicators in the last six months: turnover, profit, profit margin, level of debt, cash flow, investment, level of exports, research and development, market share; existence of a development plan for the next three years; most important element to ensure the company’s development: better qualified people on the market, social and fiscal regulations more suited to the sector of activity, greater production capacity, easy access to means of financing, stricter regulation regarding competition from outside the EU, advice and support service for the development of the company; use of selected types of financing in the past: overdraft, leasing or renting, discount or factoring, increase in capital dedicated to venture capital funds or to private individuals, loans shorter or longer than a 3-year term, public subsidies; approximate amount of last loan; recent request for a loan less than 25000 €; needs to be met by this loan; assessment of the difficulties to obtain a loan less than 25000 € compared to other forms of company’s financing; most important elements to resort a loan less than 25000 €: lower interest rates, simpler procedures for granting loans, less demanding on guarantee requirements, shorter delays for granting loans; assessment of the current financing of the company as sufficient; institutions contacted to obtain financing: banks, public institutions, private financing companies, leasing or renting companies, venture capital companies, private investors; expectations regarding the increase of the company’s capital within the next years; measures to increase the company’s capital: opening-up capital to private individual investors or to venture capital companies, management buy-out, going on the stock exchange, opening-up capital to the company’s employees; assessment of the access to bank loans as easy; assessment of the development of the impediments to access bank loans compared to a few years ago; reasons that impede obtaining a bank loan compared to a few years ago: interest rates are too high, banks request too much information, loan granting procedures are too long, administrative side of the loan application is very demanding; approval of the following statements: loan is needed to conclude projects, unsuitable offers from banks, risk-averseness of banks, banker understands specifics of the company’s sector, banker sufficiently supports the company in terms of its financing; assessment how the company’s needs regarding financial management are met internally; preferred sources of information on financing. Demography: information about the company: number of employees, development of the number of employees since 2004; company size; main activity of the company; year of company establishment; shareholding of the company; turnover of the company in the own country in the last fiscal year. Additionally coded was: country; respondent ID; language of the interview; weighting factor. Unternehmensfinanzierung. Nutzung von Krediten. Schwierigkeiten bei Kreditaufnahme. Vorgehen von Kreditinstituten in Bezug auf Finanzierungsmöglichkeiten. Finanzierungsberatung. Themen: Finanzielle Situation des Unternehmens; 3-Jahres Plan; wichtigste Maßnahmen zur Festigung des Unternehmens: qualifizierte Mitarbeiter, der Branche angepasstere soziale und steuerliche Bestimmungen, größere Produktionskapazität, einfacher Zugang zu Finanzierungsmitteln, strengere Regulierung der Konkurrenz aus Nicht-EU-Ländern, Beratung und Unterstützung für die Unternehmensentwicklung; Inanspruchnahme finanzieller Leistungen (Dispositionskredit, Leasing/Mieten, Diskont/Factoring, Kapitalerhöhung für Wagniskapitalfonds und für Privatpersonen, (Kurz-)Darlehen, öffentliche Fördermittel); Höhe des letzten Kreditantrags; Verwendungsabsicht für den Kredit; Schwierigkeiten einen Kredit unter 250.000 Euro zu bekommen im Vergleich zu anderen Finanzierungsformen; Gründe für Kreditaufnahme (niedrigere Zinssätze, einfacherer Bewilligungsvorgang, geringere Anforderung auf Kreditsicherheit, kürzere Bearbeitungszeit für Kreditbewilligung); Einschätzung der Unternehmensfinanzierung als ausreichend für Projektrealisierung; primäre Anlaufstellen für den Erhalt von Finanzmitteln; Erschließungsmöglichkeiten für Kapital um finanzielle Bedürfnisse des Unternehmens zu erfüllen; Wege für die Kapitalerschließung des Unternehmens; Einschätzung der Schwierigkeit heutzutage einen Kredit bei Banken zu bekommen im Vergleich zu früher; Gründe, warum es heutzutage schwieriger ist einen Kredit bei einer Bank zu bekommen; Einstellung zu: Kreditabhängigkeit bei Durchführung von Projekten, nicht auf die Belange des Unternehmens zugeschnittene Angebote der Banken; geringe Risikobereitschaft von Banken bei Kreditvergabe; Verständnis für die spezifischen Belange der eigenen Branche durch den zuständigen Bankangestellten; ausreichende Unterstützung bei der Finanzierung durch die Bank; Beurteilung des firmeninternen Finanzmanagements; primäre Anlaufstelle für Finanzierungsberatung. Demographie: Angaben zum Unternehmen: Anzahl der Mitarbeiter, Entwicklung der Anzahl der Beschäftigten seit 2004, Unternehmensgröße, Hauptgeschäftsfeld des Unternehmens, Gründungsjahr, Aktienanteil des Unternehmens; Jahresumsatz des Unternehmens im letzten Geschäftsjahr. Zusätzlich verkodet wurde: Land; Befragten-ID; Interviewsprache; Gewichtungsfaktor.

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.

S&P 500: A Bull or a Bear?

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

The Industrial Revenue Bond program (IRB) provides access to tax-exempt financing to help businesses and non-profit organizations renovate and build new construction, make tenant improvements, and purchase capital by securing interest rates up 4% lower than a traditional commercial loan. IRBs can be used to finance, refinance, and reimburse the costs of acquiring, constructing, restoring, rehabilitating, expanding, improving, equipping, or furnishing real property and related subordinate facilities. More than $9.5 billion has been issued through Washington, DC's IRB program since 1994.