By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

United States FRB Recession Risk data was reported at 0.178 % in Apr 2025. This records a decrease from the previous number of 0.192 % for Mar 2025. United States FRB Recession Risk data is updated monthly, averaging 0.193 % from Jan 1973 (Median) to Apr 2025, with 628 observations. The data reached an all-time high of 1.000 % in Oct 2008 and a record low of 0.022 % in Jul 2003. United States FRB Recession Risk data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S090: FRB Recession Risk.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Jun 2025 about recession indicators, academic data, and USA.

Graph and download economic data for GDP-Based Recession Indicator Index (JHGDPBRINDX) from Q4 1967 to Q4 2024 about recession indicators, percent, GDP, and indexes.

United States FRB Recession Risk: Excess Bond Premium data was reported at -0.105 Basis Point in Apr 2025. This records a decrease from the previous number of -0.060 Basis Point for Mar 2025. United States FRB Recession Risk: Excess Bond Premium data is updated monthly, averaging -0.056 Basis Point from Jan 1973 (Median) to Apr 2025, with 628 observations. The data reached an all-time high of 3.539 Basis Point in Oct 2008 and a record low of -1.026 Basis Point in Jul 2003. United States FRB Recession Risk: Excess Bond Premium data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S090: FRB Recession Risk.

United States FRB Recession Risk: Corporate Bond Credit Spread data was reported at 1.268 Basis Point in Apr 2025. This records an increase from the previous number of 1.114 Basis Point for Mar 2025. United States FRB Recession Risk: Corporate Bond Credit Spread data is updated monthly, averaging 1.572 Basis Point from Jan 1973 (Median) to Apr 2025, with 628 observations. The data reached an all-time high of 7.924 Basis Point in Nov 2008 and a record low of 0.563 Basis Point in Oct 1978. United States FRB Recession Risk: Corporate Bond Credit Spread data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S090: FRB Recession Risk.

The excess bond premium (EBP) is a measure of investor sentiment or risk appetite in the corporate bond market. A credit spread index can be decomposed into two components: a component that captures the systematic movements in default risk of individual firms and a residual component: the excess bond premium that represents variation in the average price of bearing exposure to US corporate credit risk, above and beyond the compensation for expected defaults. The EBP component of corporate bond credit spreads that is not directly attributable to expected default risk provides an effective measure of investor sentiment or risk appetite in the corporate bond market.

Graph and download economic data for Sahm Rule Recession Indicator (SAHMCURRENT) from Mar 1949 to Jun 2025 about recession indicators, academic data, and USA.

The 2020 recession did not follow the trend of previous recessions in the United States because only six months elapsed between the yield curve inversion and the 2020 recession. Over the last five decades, 12 months, on average, has elapsed between the initial yield curve inversion and the beginning of a recession in the United States. For instance, the yield curve inverted initially in January 2006, which was 22 months before the start of the 2008 recession. A yield curve inversion refers to the event where short-term Treasury bonds, such as one or three month bonds, have higher yields than longer term bonds, such as three or five year bonds. This is unusual, because long-term investments typically have higher yields than short-term ones in order to reward investors for taking on the extra risk of longer term investments. Monthly updates on the Treasury yield curve can be seen here.

FRB Recession Risk在2025-04达0.178%，相较于2025-03的0.192%有所下降。FRB Recession Risk数据按月度更新，1973-01至2025-04期间平均值为0.193%，共628份观测结果。该数据的历史最高值出现于2008-10，达1.000%，而历史最低值则出现于2003-07，为0.022%。CEIC提供的FRB Recession Risk数据处于定期更新的状态，数据来源于Federal Reserve Board，数据归类于Global Database的美国 – Table US.S090: FRB Recession Risk。

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.

LON:ETX Stock: Are We Headed for a Recession?

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

The Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.

Subprime and the collapse of the U.S. mortgage market

The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.

Market Panic and The Great Recession

As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.

This map shows which areas have concentrations of high risk businesses in the event of an economic downturn. Areas in red have a higher concentration of one or more of the five categories (by NAICS code): Clothing/Accessory stores, General Merchandise stores, Arts/Entertainment/Recreation, Accommodation, and Food Service/Drinking Places. The popup breaks down count of businesses per category and percent of businesses for the area. Data is 2019 vintage and available by county, tract, and block group. Overall, in the US, these 5 categories make up 11.8% of total businesses.Esri's Business Summary Data: Esri's Business Locations data is extracted from a comprehensive list of businesses licensed from Infogroup. It summarizes the comprehensive list of businesses from Infogroup for select NAICS and SIC summary categories by geography and includes total number of businesses, total sales, and total number of employees for a trade area.Esri's U.S. 2019 Data: Population, age, income, race, home value, spending, business, and market potential are among the topics included in the data suite. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. To browse, all data variables available within Esri's demographics explore the Data Browser. Additional Esri Resources:Get StartedEsri DemographicsU.S. 2019 Esri Updated DemographicsBusiness Summary DataMethodologies

This research investigates the strategic responses adopted by Hungarian small and medium-sized enterprises (SMEs) in the face of an adverse socio-economic environment. Focusing specifically on B2B firms, the study examines how the current crisis compels managers to mitigate the risk of buyer attrition. This analysis leads to the identification of four distinct adaptation strategies employed by these firms. Furthermore, the research delves into the concept of ‘upgrading’ challenging the prevailing notion that product development is the sole pathway for upgrading. The results suggest that effective upgrading can be initiated from various aspects of the firm’s operations. This novel perspective suggests that upgrading can be a viable strategy for firms seeking to escape from a captive supplier position, by improving either functionality or resilience. This is a new perspective that goes beyond what could be expected from the literature. The findings provide useful insights for policymakers and practitioners seeking to improve the performance of SMEs in the current dynamically shifting B2B landscape.

As of April 16, 2025, the yield for a ten-year U.S. government bond was 4.34 percent, while the yield for a two-year bond was 3.86 percent. This represents an inverted yield curve, whereby bonds of longer maturities provide a lower yield, reflecting investors' expectations for a decline in long-term interest rates. Hence, making long-term debt holders open to more risk under the uncertainty around the condition of financial markets in the future. That markets are uncertain can be seen by considering both the short-term fluctuations, and the long-term downward trend, of the yields of U.S. government bonds from 2006 to 2021, before the treasury yield curve increased again significantly in the following years. What are government bonds? Government bonds, otherwise called ‘sovereign’ or ‘treasury’ bonds, are financial instruments used by governments to raise money for government spending. Investors give the government a certain amount of money (the ‘face value’), to be repaid at a specified time in the future (the ‘maturity date’). In addition, the government makes regular periodic interest payments (called ‘coupon payments’). Once initially issued, government bonds are tradable on financial markets, meaning their value can fluctuate over time (even though the underlying face value and coupon payments remain the same). Investors are attracted to government bonds as, provided the country in question has a stable economy and political system, they are a very safe investment. Accordingly, in periods of economic turmoil, investors may be willing to accept a negative overall return in order to have a safe haven for their money. For example, once the market value is compared to the total received from remaining interest payments and the face value, investors have been willing to accept a negative return on two-year German government bonds between 2014 and 2021. Conversely, if the underlying economy and political structures are weak, investors demand a higher return to compensate for the higher risk they take on. Consequently, the return on bonds in emerging markets like Brazil are consistently higher than that of the United States (and other developed economies). Inverted yield curves When investors are worried about the financial future, it can lead to what is called an ‘inverted yield curve’. An inverted yield curve is where investors pay more for short term bonds than long term, indicating they do not have confidence in long-term financial conditions. Historically, the yield curve has historically inverted before each of the last five U.S. recessions. The last U.S. yield curve inversion occurred at several brief points in 2019 – a trend which continued until the Federal Reserve cut interest rates several times over that year. However, the ultimate trigger for the next recession was the unpredicted, exogenous shock of the global coronavirus (COVID-19) pandemic, showing how such informal indicators may be grounded just as much in coincidence as causation.

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.

DTRTU Stock: Are We Headed for a Recession?

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

Prior to the 2020 recession, the yield curve was only inverted for 141 days, which was much shorter than the average 248 days preceding the previous five U.S. recessions. For instance, the yield curve was inverted for 235 days between the inversion in January 2006 and the start of the 2007-2009 recession. A yield curve inversion refers to the event where short-term Treasury bonds, such as one or three month bonds, have higher yields than longer term bonds, such as three or five year bonds. This is unusual, because long-term investments typically have higher yields than short-term ones in order to reward investors for taking on the extra risk of longer term investments. Monthly updates on the Treasury yield curve can be seen here.

In December 2024, the yield on a 10-year U.S. Treasury note was **** percent, forecasted to decrease to reach **** percent by August 2025. Treasury securities are debt instruments used by the government to finance the national debt. Who owns treasury notes? Because the U.S. treasury notes are generally assumed to be a risk-free investment, they are often used by large financial institutions as collateral. Because of this, billions of dollars in treasury securities are traded daily. Other countries also hold U.S. treasury securities, as do U.S. households. Investors and institutions accept the relatively low interest rate because the U.S. Treasury guarantees the investment. Looking into the future Because these notes are so commonly traded, their interest rate also serves as a signal about the market’s expectations of future growth. When markets expect the economy to grow, forecasts for treasury notes will reflect that in a higher interest rate. In fact, one harbinger of recession is an inverted yield curve, when the return on 3-month treasury bills is higher than the ten-year rate. While this does not always lead to a recession, it certainly signals pessimism from financial markets.

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.

ITQRW Stock: Are We Headed for a Recession?

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

Introduction: With the worsening global climate problem, carbon peak and carbon neutrality have become crucial objects to realize sustainable development. Regarded as the engine of economic development in the new era, it is worth exploring whether digitalization could contribute to carbon goals. Meanwhile, carbon reduction should not be advocated at the cost of economic recession and contains the risk of reversal when the economy renews in growth.Material and Methods: This paper evaluates carbon efficiency using the total factor non-radial directional distance function, which reflects the economic performance and environmental performance of 246 China’s prefecture-level cities during 2011–2019. Fixed effect and mediation effect models are used to explore the non-linear relationship and transmission channels between digital development and carbon efficiency.Results: It is found that: 1) digital development would hinder carbon efficiency first and then promote it after reaching a certain level; 2) digital development could indirectly affect carbon efficiency through industrial agglomeration, industrial structure upgrading, and industrial electricity productivity in non-linear ways. 3) Heterogeneity exists in the relationship between digital development and carbon efficiency due to different regions and development types.Discussion: Due to digital development itself having high carbon-negative externalities at the initial stage, its impact on carbon efficiency is complex and non-liner even when decomposing through multiple channels. A well-structured development strategy is needed during the digitalization process in order to prompt carbon efficiency.