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 the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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?

The dataset shows structure of interest rates

Note: 1. For the year 1995-96, interest rate on deposits of maturity above 3 years, and from 1996-97 onwards, interest rates on deposit for all the maturities refer to the deposit rates of 5 major public sector banks as at end-March. 2. From 1994-95 onwards, data on minimum general key lending rates prescribed by RBI refers to the prime lending rates of 5 major public sector banks. 3. For 2011-12, data on deposit rates and Base rates of 5 major public sector banks refer to the period up to July 31, 2010. From July 1, 2010 BPLR System is replaced by Base Rate System. Accordingly the data reflects the Base Rate of five major public sector banks. Data for 2010-11 for Call/Notice Money rates are average of April-July 2010. 4. Data for dividend rate and yield rate for units of UTI are based on data received from Unit Trust of India. 5. Data on annual(gross) redemption yield of Government of India securities are based on redemption yield which is computed from 2000-01 as the mean of the daily weighted average yield of the transactions in each traded security. The weight is calculated as the share of the transaction in a given security in the aggregated value. 6. Data on prime lending rates for IDBI, IFCI and ICICI for the year 1999-00 relates to long-term prime lending rates in January 2000. 7. Data on prime lending rates for State Financial Corporation for all the years and for other term lending institutions from 2002-03 onwards relate to long-term (over 36-month) PLR. 8. Data on prime lending rate of IIBI/ IRBI from 2003-04 onwards relate to single PLR effective July 31, 2003. 9. IDBI ceased to be term lending institution on its conversion into a banking entity effective October 11, 2004. 10. ICICI ceased to be a term-lending institution after its merger with ICICI Bank. 11. Figures in brackets indicate lending rate charged to small-scale industries. 12. IFCI has become a non-bank financial company. 13. IIBI is in the process of voluntary winding up. 14. Figures for 2015-16 are as on July 14, 2015. 15. 2024-25 data : As on September 1, 2024; except for WALRs, WADTDR and 1-year median MCLR (July 2023). 16. * : Data on deposit and lending rates relate to five major Public Sector Banks up to 2003-04. While for the subsequent years, they relate to five major banks. 17. # : Savings deposit rate from 2011-12 onwards relates to balance up to 1 lakh. Savings deposit rate was deregulated with effect from October 25, 2011. 18. $ : Data on Weighted Average Lending Rates (WALRs), weighted Average Domestic Term Deposit Rate (WADTDR) and 1-year median marginal cost of funds-based lending rate (MCLR) pertain to all scheduled commercial banks (excluding RRBs and SFBs). 19. Data on lending rates in column (7) relate to Benchmark Prime Lending Rate (BPLR) for the period 2004-05 to 2009-10; Base Rate for 2010-11 to 2015-16 and Marginal Cost of Funds Based Lending Rate (MCLR) (overnight) for 2016-17 onwards. BPLR system was replaced by the Base Rate System from July 1, 2010, which, in turn, was replaced by the MCLR System effective April 1, 2016.

In the context of predicting the term structure of interest rates, we explore the marginal predictive content of real-time macroeconomic diffusion indexes extracted from a data rich real-time data set, when used in dynamic Nelson-Siegel (NS) models of the variety discussed in Svensson (NBER technical report, 1994; NSS) and Diebold and Li (Journal of Econometrics, 2006, 130, 337-364; DNS). Our diffusion indexes are constructed using principal component analysis with both targeted and untargeted predictors, with targeting done using the lasso and elastic net. Our findings can be summarized as follows. First, the marginal predictive content of real-time diffusion indexes is significant for the preponderance of the individual models that we examine. The exception to this finding is the post Great Recession period. Second, forecast combinations that include only yield variables result in our most accurate predictions, for most sample periods and maturities. In this case, diffusion indexes do not have marginal predictive content for yields and do not seem to reflect unspanned risks. This points to the continuing usefulness of DNS and NSS models that are purely yield driven. Finally, we find that the use of fully revised macroeconomic data may have an important confounding effect upon results obtained when forecasting yields, as prior research has indicated that diffusion indexes are often useful for predicting yields when constructed using fully revised data, regardless of whether forecast combination is used, or not. Nevertheless, our findings also underscore the potential importance of using machine learning, data reduction, and shrinkage methods in contexts such as term structure modeling.

These rates are the daily secondary market quotation on the most recently auctioned Treasury Bills for each maturity tranche (4-week, 13-week, 26-week, and 52-week) that Treasury currently issues new Bills. Market quotations are obtained at approximately 3:30 PM each business day by the Federal Reserve Bank of New York. The Bank Discount rate is the rate at which a Bill is quoted in the secondary market and is based on the par value, amount of the discount and a 360-day year. The Coupon Equivalent, also called the Bond Equivalent, or the Investment Yield, is the bill's yield based on the purchase price, discount, and a 365- or 366-day year. The Coupon Equivalent can be used to compare the yield on a discount bill to the yield on a nominal coupon bond that pays semiannual interest.

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.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

CSI: Expected Interest Rates: Next Yr data was reported at 28.000 1966=100 in May 2018. This records a decrease from the previous number of 32.000 1966=100 for Apr 2018. CSI: Expected Interest Rates: Next Yr data is updated monthly, averaging 59.000 1966=100 from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 133.000 1966=100 in Jun 1980 and a record low of 18.000 1966=100 in May 2004. CSI: Expected Interest Rates: Next Yr 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?

The benchmark interest rate in Mexico was last recorded at 8.50 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - small-sized enterprise - earmarked credit - Working capital with maturity up to 365 days Source: Credit Information System 27287-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---earmar 27287-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---earmar 0 0 Feedback Thank you! Close Feedback. Sorry. Tell us what happen. The data is out of date. I was unable to access the dataset (specify the resource). Insufficient documentation to understand the data set. The data contains error or inconsistency. Describe Your assessment will be sent to the e-Ouv system as a complaint. Click here if you want to track your progress. Name Email Your statement was sent to the e-Ouv system. Click here for details. Protocol number: Access code: Send to e-Ouv system Close

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.; ;

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - small-sized enterprise - nonearmarked credit - Working capital with maturity up to 365 days Source: Credit Information System 26546-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---nonear 26546-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---nonear 0 0 Feedback Thank you! Close Feedback. Sorry. Tell us what happen. The data is out of date. I was unable to access the dataset (specify the resource). Insufficient documentation to understand the data set. The data contains error or inconsistency. Describe Your assessment will be sent to the e-Ouv system as a complaint. Click here if you want to track your progress. Name Email Your statement was sent to the e-Ouv system. Click here for details. Protocol number: Access code: Send to e-Ouv system Close

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.

This table contains 39 series, with data for starting from 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

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.; ;

We use Bayesian time-varying parameter structural vector autoregressions with stochastic volatility to investigate changes in reduced-form and structural correlations between inventories and either sales growth or the real interest rate in the USA during both the inter-war and post-World War II periods. We identify four structural shocks by combining a single long-run restriction to identify a permanent output shock with three sign restrictions to identify demand? and supply-side transitory shocks. We show that during both the inter-war and post-war periods the structural correlation between inventories and real interest rate conditional on identified interest rate shocks is systematically positive; the reduced-form correlation between the two series is positive during the post-war period, but in line with the predictions of theory it is robustly negative during the inter-war era; during that era the correlations between inventories and either of the two other series exhibit a remarkably strong co-movement with output at business cycle frequencies.

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.

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.

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend