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 Russia was last recorded at 18 percent. This dataset provides the latest reported value for - Russia 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 Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset Description

This dataset contains the actual and predicted federal funds target rate for the United States from 1990 to 2023. The federal funds target rate is the interest rate at which depository institutions lend their excess reserves to each other overnight. It is set by the Federal Open Market Committee (FOMC) and is a key tool used by the Federal Reserve to influence the economy.

The dataset includes the following five columns:

Release Date: The date on which the data was released by the Federal Reserve. Time: The time of day at which the data was released. Actual: The actual federal funds target rate. Predicted: The predicted federal funds target rate. Forecast: The forecast federal funds target rate.

Data Usage

This dataset can be used for a variety of purposes, including: - Analyzing trends in the federal funds target rate over time. - Forecasting the future path of the federal funds target rate. - Assessing the effectiveness of monetary policy. - Data Quality

The data for this dataset is of high quality. The Federal Reserve is a reputable source of data and the data is updated regularly.

Data Limitations

The data for this dataset is limited to the United States. Additionally, the data does not include information on the factors that influenced the Federal Open Market Committee's decision to set the federal funds target rate.

United States US: Lending Interest Rate data was reported at 3.512 % pa in 2016. This records an increase from the previous number of 3.260 % pa for 2015. United States US: Lending Interest Rate data is updated yearly, averaging 6.922 % pa from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 18.870 % pa in 1981 and a record low of 3.250 % pa in 2014. United States US: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.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.; ;

Interest is charged if payment is not received by the due date. Remember: if the due date falls on a weekend or holiday, your payment is due the next working day.

The Ministry of Finance also applies interest to amounts the ministry owes to individuals and corporations.

Tax interest is compounded daily and interest rates are reset every 3 months.

Note: Provincial land tax interest rates are not reset every three months. Provincial land tax interest rates are summarized on the "https://www.ontario.ca/document/provincial-land-tax">provincial land tax webpage.

Note: Interest rates do not apply to the Estate Administration Tax Act, 1998.

Current interest rates (July 1, 2025 to September 30, 2025):

• 8% on taxes you owe to the ministry
• 2% on taxes you overpaid
• 5% on taxes or refunds you are eligible for as a result of a successful appeal or objection
• 6% on late International Fuel Tax Agreement payments
• 6% on International Fuel Tax Agreement refunds the ministry has not paid you within 90 days

You can download the dataset to view the historical tax interest rates.

Non-Resident Speculation Tax (NRST)

(1) Interest on tax you overpaid begins to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid.

(2) On refunds you are eligible for as a result of a successful appeal or objection of a NRST refund/rebate disallowance, the interest rate is the same rate as though you had overpaid and will begin to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. Refunds as a result of a successful appeal or objection of NRST that was paid pursuant to a Notice of Assessment, interest will accrue at the higher appeals/objection rate, beginning to accrue from the date of payment to the date the rebate or refund is paid.

The benchmark interest rate in Venezuela was last recorded at 59.27 percent. This dataset provides the latest reported value for - Venezuela Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains a wealth of information from 52,000 loan applications, offering detailed insights into the factors that influence loan approval decisions. Collected from financial institutions, this data is highly valuable for credit risk analysis, financial modeling, and predictive analytics. The dataset is particularly useful for anyone interested in applying machine learning techniques to real-world financial decision-making scenarios.

Overview: This dataset provides information about various applicants and the loans they applied for, including their demographic details, income, loan terms, and approval status. By analyzing this data, one can gain an understanding of which factors are most critical for determining the likelihood of loan approval. The dataset can also help in evaluating credit risk and building robust credit scoring systems.

Dataset Columns: Applicant_ID: Unique identifier for each loan application. Gender: Gender of the applicant (Male/Female). Age: Age of the applicant. Marital_Status: Marital status of the applicant (Single/Married). Dependents: Number of dependents the applicant has. Education: Education level of the applicant (Graduate/Not Graduate). Employment_Status: Employment status of the applicant (Employed, Self-Employed, Unemployed). Occupation_Type: Type of occupation, which provides insights into the nature of the applicant’s job (Salaried, Business, Others). Residential_Status: Type of residence (Owned, Rented, Mortgage). City/Town: The city or town where the applicant resides. Annual_Income: The total annual income of the applicant, a key factor in loan eligibility. Monthly_Expenses: The monthly expenses of the applicant, indicating their financial obligations. Credit_Score: The applicant's credit score, reflecting their creditworthiness. Existing_Loans: Number of existing loans the applicant is servicing. Total_Existing_Loan_Amount: The total amount of all existing loans the applicant has. Outstanding_Debt: The remaining amount of debt yet to be paid by the applicant. Loan_History: The applicant’s previous loan history (Good/Bad), indicating their repayment reliability. Loan_Amount_Requested: The loan amount the applicant has applied for. Loan_Term: The term of the loan in months. Loan_Purpose: The purpose of the loan (e.g., Home, Car, Education, Personal, Business). Interest_Rate: The interest rate applied to the loan. Loan_Type: The type of loan (Secured/Unsecured). Co-Applicant: Indicates if there is a co-applicant for the loan (Yes/No). Bank_Account_History: Applicant’s banking history, showing past transactions and reliability. Transaction_Frequency: The frequency of financial transactions in the applicant’s bank account (Low/Medium/High). Default_Risk: The risk level of the applicant defaulting on the loan (Low/Medium/High). Loan_Approval_Status: Final decision on the loan application (Approved/Rejected).

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.

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).

TARGET VARIABLE

The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.

EXPLANATORY VARIABLES

The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.

FULL LIST OF VARIABLES

Loan identification variables:

id: Loan id (unique identifier).

issue_d: Month and year in which the loan was approved.

Quantitative variables:

revenue: Borrower's self-declared annual income during registration.

dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.

loan_amnt: Amount of credit requested by the borrower.

fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.

experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

purpose: Credit purpose category for the loan request.

home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.

addr_state: Borrower's residence state from the USA.

zip_code: Zip code of the borrower's residence.

Textual variables

title: Title of the credit request description provided by the borrower.

desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&” was substituted by “&”, “<” was substituted by “<”, etc.).

RELATED WORKS

This dataset has been used in the following academic articles:

Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458

Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412

Tax interest is compounded daily and interest rates are reset every 3 months. Note: Provincial land tax interest rates are not reset every three months. Provincial land tax interest rates are summarized on the provincial land tax webpage. Interest rates do not apply to the Estate Administration Tax Act, 1998. Current interest rates (January 1, 2025 to March 31, 2025): * 9% on taxes you owe to the ministry * 3% on taxes you overpaid * 6% on taxes or refunds you are eligible for as a result of a successful appeal or objection * 6% on late International Fuel Tax Agreement payments * 6% on International Fuel Tax Agreement refunds the ministry has not paid you within 90 days You can download the dataset to view the historical tax interest rates. Non-Resident Speculation Tax (NRST) (1) Interest on tax you overpaid begins to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. (2) On refunds you are eligible for as a result of a successful appeal or objection of a NRST refund/rebate disallowance, the interest rate is the same rate as though you had overpaid and will begin to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. Refunds as a result of a successful appeal or objection of NRST that was paid pursuant to a Notice of Assessment, interest will accrue at the higher appeals/objection rate, beginning to accrue from the date of payment to the date the rebate or refund is paid.

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

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    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.

Bangladesh BD: Lending Interest Rate data was reported at 9.852 % pa in 2024. This records an increase from the previous number of 7.570 % pa for 2023. Bangladesh BD: Lending Interest Rate data is updated yearly, averaging 12.219 % pa from Dec 1976 (Median) to 2024, with 49 observations. The data reached an all-time high of 14.846 % pa in 1990 and a record low of 7.121 % pa in 2022. Bangladesh BD: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.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.;;

Small Business Administration Loan Guarantee (SBA)

1.5 Million Loan Guarantees Across 7(a) and 504 Programs

By Noah Brod [source]

About this dataset

The Small Business Administration (SBA) Loan Guarantee Data provides a comprehensive look at loans that were approved by the SBA from January 1, 1990 to December 31, 1999. This dataset offers insight into roughly 1.5 million approved loans, including details such as the loan amount, interest rate, project county, and more.

This data was collected as part of an FOIA request and is updated quarterly for up-to-date information. It should be noted that the SBA is not a direct lender but rather a guarantor of the loan which is made by either a bank or non-bank lender.

The dataset includes detailed fields such as AsOfDate, Program Type, Gross Approval Amounts and Initial Interest Rates; Fanchise Codes and County Information; Delivery Method and Status Reports; Congressional Districts involved in financing these loans; Jobs Supported as part of each loan; Billing Information related to ChargeOff Dates and Amounts; SBADistrict Office associated with individual loan approvals ;and more!

This unique pool of data can offer invaluable insights into the mechanisms behind small business lending throughout the nineties in America – showing how much has changed since then but also how some trends remain consistent over time. The Small Business Administration Loan Guarantee Data can help shine light on important topics such as demographic disparities among borrowers or regional differences between approving offices - increasing our understanding of American business practices overall!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Using NaicsCode, initialize a visual representation of the most popular types of businesses that receive SBA loan ensures to get a better sense of which industries are the biggest uses for this financing program.
• Calculating Loan Status data over a period of time to analyse trends in terms of loan defaults and how much money is disbursed vs charged off.
• Examining GrossApproval and SBAGuarterNeedApproval data to determine which zipcodes or states have received more funding from the SBA and apply this information in aid targeting certain areas as part of govermental stimulus packages during tough economic times

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 7a_504_FOIA%20Data%20Dictionary.csv

File: FOIA%20-%207(a)(FY1991-FY1999).csv | Column name | Description | |:--------------------------|:-------------------------------------------------------------| | AsOfDate | Date the data was last updated. (Date) | | Program | Type of loan program. (String) | | BorrName | Name of the borrower. (String) | | BorrStreet | Street address of the borrower. (String) | | BorrCity | City of the borrower. (String) | | BorrState | State of the borrower. (String) | | BorrZip | Zip code of the borrower. (String) | | BankName | Name of the bank. (String) | | BankStreet | Street address of the bank. (String) | | BankCity | City of the bank. (String) | | BankState | State of the bank. (String) | | BankZip | Zip code of the bank. (String) | | GrossApproval | Total amount of the loan approved. (Number) | | SBAGuaranteedApproval | Amount of the loan guaranteed by the SBA. (Number) | | ApprovalDate | Date the loan was approved. (Date) | | ApprovalFiscalYear | Fiscal year the loan was approved. (Number) | | FirstDisbursementDate | Date the loan was disbursed. (Date) | | DeliveryMethod | Method of delivery for the loan. (String) | | subpgmdesc | Description of the loan program. (String) ...

Monetary policy is generally regarded as a central element in the attempts of policy makers to attenuate business-cycle fluctuations. According to the New Keynesian paradigm, central banks are able to stimulate or depress aggregate demand in the short run by adjusting their nominal interest rate targets. The effects of interest rate changes on aggregate consumption, the largest component of aggregate demand, are well understood in the context of this paradigm, on which the canonical "workhorse'' model used in monetary policy analysis is grounded. A key feature of the model is that aggregate consumption is fully described by the amount of goods consumed by a representative household. A decline in the policy rate for instance implies that the real interest rate declines, the representative household saves less and hence increase its demand for consumption. At the same time, general equilibrium effects let labour income grow causing consumption to increase further. However, the mechanism outlined above ignores a considerable amount of empirically-observed heterogeneity among households. For example, households with a higher earnings elasticity to interest rate changes benefit more from a rate cut than those with a lower elasticity; households with large debt positions are at a relative advantage over households with large bond holdings; and households with low exposure to inflation are relatively better off than those holding a sizeable amount of nominal assets. As a result, the contribution to the aggregate consumption response differs substantially across households, implying that monetary expansions and tightenings produce relative "winners'' and relative "losers''. The aim of the project laid out in this proposal is to give a disaggregated account of the heterogeneous effects of monetary-policy induced interest rate changes on household consumption and a detailed analysis of the channels underlying them. Additionally, it seeks to draw conclusions about the determinants of the strength of the transmission mechanism of monetary policy. To do so, it relies on a large panel comprising detailed data from the universe of all households residing in Norway between 1993 and 2015 supplemented with additional micro-data provided by the European Commission. I will be assisted by two project partners, Pascal Paul who is a member of the Research Department of the Federal Reserve Bank of San Francisco and Martin Holm who is affiliated with the Research Unit of Statistics Norway and the University of Oslo. In addition, I would like to collaborate with and help train a doctoral student based at the University of Lausanne on this project. Existing empirical studies of the consumption response to monetary policy at the micro level rely on survey data. Therefore, they are subject to a number of severe data limitations. The surveys employed typically have either no or only a short panel dimension, suffer from attrition, include only limited information on income and wealth, are top-coded, and contain a significant amount of measurement error. The administrative data set provided to us by Statistics Norway suffers from none of these issues, implying that we are in a unique position to evaluate the household-level effects of policy rate changes. In a first step, we use forecasts published by the Norwegian central bank to derive monetary policy shocks that are robust to the simultaneity problem inherent in the identification of the effects of monetary policy following Romer and Romer (2004). We then confront the micro-data with the estimated shocks to study the consumption response along different segments of the income and wealth distribution and to test the importance of heterogeneity in labour earnings, financial income, liquid assets, inflation exposure and interest rate exposure among others. The findings will be of high relevance as they will not only allow us to evaluate channels hypothesised in the analytical literature, improve our understanding of the monetary policy transmission mechanism and its distributional consequences but also serve as a benchmark for structural models built both by theorists and practitioners.

Moldova MD: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 3.558 % pa in 2017. This records an increase from the previous number of -1.145 % pa for 2016. Moldova MD: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 6.014 % pa from Dec 1996 (Median) to 2017, with 22 observations. The data reached an all-time high of 17.539 % pa in 2002 and a record low of -8.014 % pa in 1996. Moldova MD: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

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.

Brazil Lending Rate: per Annum: Pre-Fixed: Individuals: Leasing of Vehicles: Banco Capital S.A. data was reported at 0.000 % pa in 03 Jul 2019. This stayed constant from the previous number of 0.000 % pa for 02 Jul 2019. Brazil Lending Rate: per Annum: Pre-Fixed: Individuals: Leasing of Vehicles: Banco Capital S.A. data is updated daily, averaging 0.000 % pa from Jan 2012 (Median) to 03 Jul 2019, with 1641 observations. The data reached an all-time high of 0.000 % pa in 03 Jul 2019 and a record low of 0.000 % pa in 03 Jul 2019. Brazil Lending Rate: per Annum: Pre-Fixed: Individuals: Leasing of Vehicles: Banco Capital S.A. data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Interest and Foreign Exchange Rates – Table BR.MB029: Lending Rate: per Annum: by Banks: Pre-Fixed: Individuals: Leasing of Vehicles. Lending Rate: Daily: Interest rates disclosed represent the total cost of the transaction to the client, also including taxes and operating. These rates correspond to the average fees in the period indicated in the tables. There are presented only institutions that had granted during the period determined. In general, institutions practicing different rates within the same type of credit. Thus, the rate charged to a customer may differ from the average. Several factors such as the time and volume of the transaction, as well as the guarantees offered, explain the differences between interest rates. Certain institutions grant allowance of the use of the term overdraft. However, this is not considered in the calculation of rates of this type. It should be noted that the overdraft is a modality that has high interest rates. Thus, its use should be restricted to short periods. If the customer needs resources for a longer period, should find ways to offer lower rates. The Brazilian Central Bank publishes these data with a delay about 20 days with relation to the reference period, thus allowing sufficient time for all Financial Institutions to deliver the relevant information. Interest rates presented in this set of tables correspond to averages weighted by the values of transactions conducted in the five working days specified in each table. These rates represent the average effective cost of loans to customers, consisting of the interest rates actually charged by financial institutions in their lending operations, increased tax burdens and operational incidents on the operations. The interest rates shown are the average of the rates charged in the various operations performed by financial institutions, in each modality. In one discipline, interest rates may differ between customers of the same financial institution. Interest rates vary according to several factors, such as the value and quality of collateral provided in the operation, the proportion of down payment operation, the history and the registration status of each client, the term of the transaction, among others . Institutions with “zero” did not operate on modalities for those periods or did not provide information to the Central Bank of Brazil. The Central Bank of Brazil assumes no responsibility for delay, error or other deficiency of information provided for purposes of calculating average rates presented in this

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

The Turkish Lira is losing its value against U.S. Dollar constantly. As of October 22, 2021, 1 USD = 9.61 Turkish Lira (TRY). On the other hand, interest rates are quite high, especially for the Turkish Lira.

I set out to investigate if I had $100000 in 2010 and invested this money in different interest rates in both Turkish Lira (TRY) and US Dollar (USD), which investment would bring more gain in 2021.

Content

The data has been gathered from Türkiye Cumhuriyeti Merkez Bankasi (TCMB), aka the Turkish FED, website. The data shows the historical interest rates as well as USD/TRY conversion rates between July 2010 and July 2021. The original data’s all column names and relative explanations were Turkish, so the columns are renamed and the data is cleaned.

There are ten cleaned columns on the dataset: Date, 1-month TRY interest rates, 3 months TRY interest rates, 6 months TRY interest rates, 1-year TRY interest rates, 1 month USD interest rates, 3 months USD interest rates, 6 months USD interest rates, 1 year USD interest rates, USD/TRY Buying Conversion Rate, USD/TRY Selling Conversion Rate.

** USD Buying means, the customer is selling USD to the bank/ exchange office ** USD Selling means, the customer is buying USD to the bank/ exchange office

Inspiration

Would it be more beneficial if I converted my $100000 in July 2010 to Turkish Lira, which is the equivalent of 153631.36 TRY using July 2010’s rates and invested with Turkish high-interest rates or kept my money as U.S. Dollars and invested with relatively lower U.S. Dollar interest rates until July 2021? $100000 is equivalent to 861294.12 TRY in July 2021.