In response to the COVID-19 crisis, the Board's emergency lending facilities have provided a critical backstop. The Board launched a centralized 13(3) Lending Facilities Data Repository on November 6, 2020 to bring together the emergency lending facilities data from different systems and databases. The Federal Reserve established the Secondary Market Corporate Credit Facility (SMCCF) on March 23, 2020, to support credit to employers by providing liquidity to the market for outstanding corporate bonds. The SMCCF supports market liquidity by purchasing in the secondary market corporate bonds issued by investment grade U.S. companies or certain U.S. companies that were investment grade as of March 22, 2020, as well as U.S.-listed exchange-traded funds whose investment objective is to provide broad exposure to the market for U.S. corporate bonds. The SMCCF's purchases of corporate bonds will create a portfolio that tracks a broad, diversified market index of U.S. corporate bonds. The Treasury, using funds appropriated to the ESF through the CARES Act, will make an equity investment in an SPV established by the Federal Reserve for the SMCCF and the Primary Market Corporate Credit Facility. The SMCCF ceased purchasing eligible assets on December 31, 2020.

Graph and download economic data for Supplementary Information: Supplemental Information on 2020 Credit Facilities: Outstanding Amount of Corporate Credit Facilities LLC Asset Purchases: Wednesday Level (H41RESPPAAC2HANWW) from 2002-12-18 to 2025-07-09 about CCF, information, purchase, credits, corporate, assets, and USA.

In response to the COVID-19 crisis, the Board's emergency lending facilities have provided a critical backstop. The Board launched a centralized 13(3) Lending Facilities Data Repository on November 6, 2020 to bring together the emergency lending facilities data from different systems and databases. The Federal Reserve established the Secondary Market Corporate Credit Facility (SMCCF) on March 23, 2020, to support credit to employers by providing liquidity to the market for outstanding corporate bonds. The SMCCF supports market liquidity by purchasing in the secondary market corporate bonds issued by investment grade U.S. companies or certain U.S. companies that were investment grade as of March 22, 2020, as well as U.S.-listed exchange-traded funds whose investment objective is to provide broad exposure to the market for U.S. corporate bonds. The SMCCF's purchases of corporate bonds will create a portfolio that tracks a broad, diversified market index of U.S. corporate bonds. The Treasury, using funds appropriated to the ESF through the CARES Act, will make an equity investment in an SPV established by the Federal Reserve for the SMCCF and the Primary Market Corporate Credit Facility. The SMCCF ceased purchasing eligible assets on December 31, 2020.

This file contains the code and pseudo-random "fake" datasets for replicating the analyses in "Bond Price Fragility and the Structure of the Mutual Fund Industry." Because the provided datasets are fake, the code will NOT produce the exact results in the paper. The replication package includes the following components: (i) All_tables.do, (ii) Variable_names.xlsx, (iii) “\results” subfolder, and (iv) “\data” subfolder. Please read "README.pdf" before running the code.

Graph and download economic data for Real federal government consumption expenditures: Nondefense consumption expenditures: Gross output of general government: Intermediate goods and services purchased: Nondurable goods: Commodity Credit Corporation inventory change (B826RX1A020NBEA) from 2007 to 2024 about nondefense, intermediate, purchase, output, inventories, nondurable goods, credits, gross, expenditures, federal, corporate, consumption expenditures, consumption, government, goods, services, commodities, real, GDP, and USA.

United States - Real federal government consumption expenditures: Nondefense consumption expenditures: Gross output of general government: Intermediate goods and services purchased: Nondurable goods: Commodity Credit Corporation inventory change was 0.02200 Bil. of Chn. 2009 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Real federal government consumption expenditures: Nondefense consumption expenditures: Gross output of general government: Intermediate goods and services purchased: Nondurable goods: Commodity Credit Corporation inventory change reached a record high of 1.00000 in January of 2001 and a record low of -1.66100 in January of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Real federal government consumption expenditures: Nondefense consumption expenditures: Gross output of general government: Intermediate goods and services purchased: Nondurable goods: Commodity Credit Corporation inventory change - last updated from the United States Federal Reserve on June of 2025.

The Securities Exchange Act of 1934 (the Act) authorizes the Board to regulate securities credit extended by brokers, dealers, banks, and other lenders. The FR T-4, FR U-1, and FR G-3 are recordkeeping requirements for brokers and dealers, banks, and other lenders, respectively. The FR G-3 and FR U-1 document the purpose of loans secured by margin stock. For purposes of these forms, margin stock is defined as (1) stocks that are registered on a national securities exchange or any over-the-counter security designated for trading in the National Market System, (2) debt securities (bonds) that are convertible into such stocks, and (3) shares of most mutual funds. The FR T-4 documents the purpose of credit being extended when that credit is not to purchase, carry, or trade in securities and the credit is in excess of that otherwise permitted under Regulation T , Credit by Brokers and Dealers. Lenders that are not brokers, dealers, and banks making loans secured by margin stock must register and deregister with the Federal Reserve using the FR G-1 and FR G-2, respectively, and must file an annual report (FR G-4) while registered. The Federal Reserve uses the data collected by the FR G-1, FR G-2, and FR G-4 to identify lenders subject to the Board’s Regulation U (Credit by Banks or Persons other than Brokers or Dealers for the Purpose of Purchasing or Carrying Margin Stocks) to verify their compliance with the regulation, and to monitor margin credit.

Graph and download economic data for Federal government consumption expenditures: Nondefense consumption expenditures: Gross output of general government: Intermediate goods and services purchased: Nondurable goods: Commodity Credit Corporation inventory change (B826RC1Q027SBEA) from Q1 1947 to Q1 2025 about nondefense, intermediate, purchase, output, inventories, nondurable goods, credits, gross, expenditures, federal, corporate, consumption expenditures, consumption, government, goods, services, commodities, GDP, and USA.

Auto leasing, loans and sales financing is comprised of establishments that provide sales financing or leasing in combination with sales financing for automobiles. During the period, growth in consumer spending drove demand for these services, while a rising prime rate drove up the cost of (and therefore revenue generated by) industry services, resulting in revenue growth. The industry experienced declines in revenue at the onset of the period as it faced depressed consumer demand and a lower prime rate due to the pandemic. The industry returned to growth in 2023 as consumer spending climbed and the prime rate was raised to combat inflation. Key economic indicators, particularly strong demand to replace aging vehicles, boded well for financiers over the past couple of years. The economic downturn at the onset of the period caused consumers to postpone large capital purchases like automobiles, which reduced demand for industry services in the same year. Revenue rebounded in 2023 as access to credit climbed. Higher interest rates in the latter part of the period limited the growth in demand for auto loans. Although in 2024, the Fed cut interest rates as inflationary pressures eased, which will boost loan demand for automobiles. In addition, the Fed is anticipated to cut rates further in 2025 which will further boost demand for automobile loans. Overall, over the past five years, industry revenue has grown at a CAGR of 2.1% to reach $172.0 billion, including a 1.5% decline in 2025 alone. Industry profit has declined over the past five years and will account for 23.9% of revenue in 2025. Growth at the onset of the outlook period will likely be limited. The Federal Reserve is anticipated to cut rates further in the latter part of the period as inflationary pressures continue to ease. Lower interest rates will provide a boost for industry services as demand for new auto loans and leases is expected to jump. In addition, the improving economic trends in access to credit, consumer confidence and rising levels of disposable income levels will provide a boost to industry revenue as consumers will be better able to afford higher-priced automobiles and new cars. Overall, industry revenue is expected to climb at a CAGR of 1.5% to $185.0 billion over the five years to 2030.

Graph and download economic data for ICE BofA US Corporate Index Effective Yield (BAMLC0A0CMEY) from 1996-12-31 to 2025-07-10 about yield, corporate, interest rate, interest, rate, and USA.

Revenue growth for the Finance and Insurance sector has varied in recent years, as a result of differing economic trends. The sector plays a vital role in facilitating necessary financial transactions between consumers, businesses and government agencies. The core services provided by operators in this sector include providing insurance products needed by businesses and consumers to legally operate corporations and assets; offering, borrowing and depository services needed to finance new projects and safely save money; and investing to create and preserve investors' assets. A wide range of operators in the sector benefited from improving macroeconomic conditions over the past five years. For example, In 2022, the Fed increased interest rates in an effort to curb historically high inflation. Although higher interest rates increased investment income from fixed-income securities for the finance and insurance sector. Recently in 2024, the Fed cut interest rates as inflationary pressured have eased. Reduced interest rates will enable consumers to borrow money at lower interest rates which will increase loan demand although reduced rates will hinder investment income from fixed-income securities for the sector. The Fed is anticipated to cut rates further in 2025, boosting loan demand but hindering interest income from each loan. In addition, the growing prevalence of emerging technologies such as AI and data analytic tools has streamlined operations and helped reduce operational costs. These tools help industry companies identify trends and potential risks more efficiently. Also the growth of mobile and digital platforms has increased customer satisfaction and accessibility, boosting demand for finance and insurance products and services. Over the past five years, industry revenue grew at a CAGR of 3.8% to $7.4 trillion, including a 2.9% jump in 2025 alone, with profit climbing to 23.6% in the same year. Sector revenue will increase at a CAGR of 2.5% to $8.4 trillion over the five years to 2030. As the economy continues to improve, per capita disposable income is expected to increase. This will likely lead to increased financial activity by consumers, which will likely be processed and facilitated by operators in the sector. The Federal Reserve is also anticipated to cut interest rates further. Reduced interest rates will reduce interest income for operators but will increase the volume of loans. In addition, the acquisition of financial technology start-ups to compete in a changing technological and financial environment will increase.

The industry is composed of non-depository institutions that conduct primary and secondary market lending. Operators in this industry include government agencies in addition to non-agency issuers of mortgage-related securities. Through 2025, rising per capita disposable income and low levels of unemployment helped fuel the increase in primary and secondary market sales of collateralized debt. Nonetheless, due to the pandemic and the sharp contraction in economic activity in 2020, revenue gains were limited, but have climbed as the economy has normalized and interest rates shot up to tackle rampant inflation. However, in 2024 the Federal Reserve cut interest rates as inflationary pressures eased and is expected to be cut further in 2025. Overall, these trends, along with volatility in the real estate market, have caused revenue to slump at a CAGR of 1.5% to $485.0 billion over the past five years, including an expected decline of 1.1% in 2025 alone. The high interest rate environment has hindered real estate loan demand and caused industry profit to shrink to 11.6% of revenue in 2025. Higher access to credit and higher disposable income have fueled primary market lending over much of the past five years, increasing the variety and volume of loans to be securitized and sold in secondary markets. An additional boon for institutions has been an increase in interest rates in the latter part of the period, which raised interest income as the spread between short- and long-term interest rates increased. These macroeconomic factors, combined with changing risk appetite and regulation in the secondary markets, have resurrected collateralized debt trading since the middle of the period. Although the FED cut interest rates in 2024, this will reduce interest income for the industry but increase loan demand. Although institutions are poised to benefit from a strong economic recovery as inflationary pressures ease, relatively steady rates of homeownership, coupled with declines in the 30-year mortgage rate, are expected to damage the primary market through 2030. Shaky demand from commercial banking and uncertainty surrounding inflationary pressures will influence institutions' decisions on whether or not to sell mortgage-backed securities and commercial loans to secondary markets. These trends are expected to cause revenue to decline at a CAGR of 0.8% to $466.9 billion over the five years to 2030.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-07-02 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

Graph and download economic data for Revolving Consumer Credit Owned and Securitized (REVOLSL) from Jan 1968 to Apr 2025 about securitized, owned, revolving, consumer credit, loans, consumer, and USA.

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.

Fed, ECB, and BoE to Pause Rate Hikes Amid Recession Fears

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Fed Takes a Breather in Battle Against Inflation

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

US Economy to Slow in June as Fed Tightens Monetary Policy

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

Consumer Credit in the United States decreased to 5.10 USD Billion in May from 16.87 USD Billion in April of 2025. This dataset provides the latest reported value for - United States Consumer Credit Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Credit unions have experienced growth in recent years, stemming from increased membership and elevated interest rates throughout the period. The industry experienced improving macroeconomic conditions over the past five years, credit unions benefitted from increased consumer borrowing. Although at the onset of the period the industry was negatively impacted by economic volatility. Economic uncertainty led consumers to limit spending, while interest rates declined because the Federal Reserve lowered the Federal Funds Rate to the zero-bound range. Revenue climbed marginally by 0.2% in 2020. However, as the Federal Reserve raised interest rates in an attempt to curb inflation in 2022, industry revenue benefited. The industry experienced greater interest income demand although loan volumes were limited. However, in the latter part of the period the Fed slashed interest rates as inflationary pressures eased, hindering interest income but boosting loan demand volumes. Overall, industry revenue swelled at a CAGR of 2.1% to $113.1 billion over the past five years, including a 1.6% jump in 2025 alone. Industry profit has also climbed due to greater interest income revenue and will comprise 19.9% of revenue in 2025. Changes in the regulatory environment have and will continue to shape the direction of this industry. Greater demand for credit unions increases their systemic importance to the overall economy. These intermediaries are federally insured, so any liquidity crisis requiring federal intervention would burden taxpayers. Legislation dictating stricter capital requirements has been passed under the National Credit Union Association's Risk-Based Capital Final Rule despite lobbying and opposition. Despite an intensified regulatory landscape, industry revenue is expected to expand at a CAGR of 0.8% to $118.0 billion over the five years to 2030. As the economy settles back to normal, consumer borrowing activity is expected to mount. The industry is also likely to endure greater competition from commercial banks, as their improving customer satisfaction threatens credit union membership. Despite this challenge, credit unions are expected to continue to receive strong demand for mortgages as the rate of a 30-year conventional mortgage is expected to decline over the next five years.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Share of Accounts Making the Minimum Payment (RCCCBSHRMIN) from Q3 2012 to Q1 2025 about shares, accounts, FR Y-14M, payments, consumer credit, large, balance, loans, consumer, banks, depository institutions, and USA.