Due to the impact of the COVID-19 pandemic, starting on **************, the U.S. federal government paused payments on federal student loans, moving billions of dollars of student debt into forbearance. Federal student loans are in forbearance, meaning that no payments need to be made, and the interest rate has been set to zero percent until ******************. However, despite these measures, student debt increased in all states. The amount of student debt increased by ** percent in Alaska between 2019 and 2020, the most out of any state.
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Graph and download economic data for Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates (PRIME) from 1955-08-04 to 2024-12-20 about prime, loans, interest rate, banks, depository institutions, interest, rate, and USA.
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Graph and download economic data for Bank Prime Loan Rate (DPRIME) from 1955-08-04 to 2025-08-07 about prime, loans, interest rate, banks, interest, depository institutions, rate, and USA.
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Concept: Average interest rate of individuals registered as MEI by type of credit - Personal loan - Payroll-deducted Source: Central Bank of Brazil - Department of Financial Education 27170-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---personal-loan---pa 27170-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---personal-loan---pa
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Concept: Average interest rate of individuals registered as MEI by type of credit - Other loans Source: Central Bank of Brazil - Department of Financial Education 27188-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---other-loans 27188-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---other-loans
According to a survey conducted in 2022, ** percent of adults said that vocational training or other professional certification programs were definitely worth the price, more than other higher education institutions. Undergraduate education at private universities, for-profit, was perceived by adults as the least likely to be worth the price out of the other types. The student debt crisis In the United States, the amount of outstanding student loan debt has skyrocketed in the last few years, ultimately outpacing all other forms of household debt. As of the first quarter of 2024, Americans owed over **** trillion U.S. dollars in student loans, likely influenced by increasing college tuition prices at a time of rising living costs and little wage growth. By the 2020/21 academic year, the average cost of attending a four-year postsecondary institution in the U.S. reached over ****** U.S. dollars, a price which may triple for Americans attending private and non-profit schools. In that same year, the average student debt for a bachelor's degree in totaled almost ****** U.S. dollars, depicting an increase in the amount of Americans taking on larger debts to attend higher education - an agreement which ultimately leads to an even greater outstanding balance from accrued interest. Despite a three-and-a-half-year pause on monthly student loan payments during the COVID-19 pandemic which aimed to alleviate the economic burden faced by over ** million borrowers, most Americans still struggle to afford these payments. Cutting out college costs As the cost of college - and the resulting student debt - remains on the rise in the U.S., more and more university graduates have been found to be struggling financially, often having difficulty affording bills and other living expenses. Such financial hardships have also caused significant disruption to the lives of younger Americans, with a 2022 survey showing that around a ******* of Gen Z were unable to save for retirement or emergencies and had to delay homeownership and having children due to their student debt. Consequently, debates have arisen over whether the benefits of higher education still exceed the costs in the U.S., with many beginning to doubt that getting a college degree is worth the financial risk. While tuition costs remain at an all-time high, it is probable that financing a college degree may be detrimental for those Americans who have fewer resources and are unable to fund higher education without going into a significant amount of debt.
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Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - individual microentrepreneur (MEI) - nonearmarked credit - Other loans Source: Central Bank of Brazil - Department of Financial Education 26913-average-interest-rate-by-source-of-funds-and-type-of-credit---individual-microentrepreneur-me 26913-average-interest-rate-by-source-of-funds-and-type-of-credit---individual-microentrepreneur-me
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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Graph and download economic data for Federal government current expenditures: Interest payments (A091RC1Q027SBEA) from Q1 1947 to Q2 2025 about payments, expenditures, federal, government, interest, GDP, and USA.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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 G.19 Statistical Release, "Consumer Credit," reports outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real estate. Total consumer credit comprises two major types: revolving and nonrevolving. Revolving credit plans may be unsecured or secured by collateral and allow a consumer to borrow up to a prearranged limit and repay the debt in one or more installments. Credit card loans comprise most of revolving consumer credit measured in the G.19, but other types, such as prearranged overdraft plans, are also included. Nonrevolving credit is closed-end credit extended to consumers that is repaid on a prearranged repayment schedule and may be secured or unsecured. To borrow additional funds, the consumer must enter into an additional contract with the lender. Consumer motor vehicle and education loans comprise the majority of nonrevolving credit, but other loan types, such as boat loans, recreational vehicle loans, and personal loans, are also included.
The G.19 also reports selected terms of credit, including interest rates on new car loans, personal loans, and credit card plans at commercial banks. Historically, the G.19 also included series that measure the terms of credit for motor vehicle loans at finance companies. In the first quarter of 2011, publication of these series was temporarily suspended because of the deterioration of their statistical foundation. The statistical foundation is in the process of being improved, and publication will resume as soon as possible.
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Graph and download economic data for Household Debt Service Payments as a Percent of Disposable Personal Income (TDSP) from Q1 1980 to Q1 2025 about disposable, payments, debt, personal income, percent, personal, households, services, income, and USA.
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Key information about United States Non Performing Loans Ratio
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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 foreclosure rate in the United States has experienced significant fluctuations over the past two decades, reaching its peak in 2010 at **** percent following the financial crisis. Since then, the rate has steadily declined, with a notable drop to **** percent in 2021 due to government interventions during the COVID-19 pandemic. In 2024, the rate stood slightly higher at **** percent but remained well below historical averages, indicating a relatively stable housing market. Impact of economic conditions on foreclosures The foreclosure rate is closely tied to broader economic trends and housing market conditions. During the aftermath of the 2008 financial crisis, the share of non-performing mortgage loans climbed significantly, with loans 90 to 180 days past due reaching *** percent. Since then, the share of seriously delinquent loans has dropped notably, demonstrating a substantial improvement in mortgage performance. Among other things, the improved mortgage performance has to do with changes in the mortgage approval process. Homebuyers are subject to much stricter lending standards, such as higher credit score requirements. These changes ensure that borrowers can meet their payment obligations and are at a lower risk of defaulting and losing their home. Challenges for potential homebuyers Despite the low foreclosure rates, potential homebuyers face significant challenges in the current market. Homebuyer sentiment worsened substantially in 2021 and remained low across all age groups through 2024, with the 45 to 64 age group expressing the most negative outlook. Factors contributing to this sentiment include high housing costs and various financial obligations. For instance, in 2023, ** percent of non-homeowners reported that student loan expenses hindered their ability to save for a down payment.
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Due to the impact of the COVID-19 pandemic, starting on **************, the U.S. federal government paused payments on federal student loans, moving billions of dollars of student debt into forbearance. Federal student loans are in forbearance, meaning that no payments need to be made, and the interest rate has been set to zero percent until ******************. However, despite these measures, student debt increased in all states. The amount of student debt increased by ** percent in Alaska between 2019 and 2020, the most out of any state.