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.

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.

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.

Between the first quarter of 2018 and the first quarter of 2022, the relative probability of recession in G7 economies was the highest in the first quarter of 2020, as a result of the COVID-19 pandemic. The risk of recession started to increase again in the first quarter of 2022, due to the escalation of the conflict between Russia and Ukraine.

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.

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.

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.

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.

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

In a 2019 analysis, Riverside, California was the most at risk of a housing downturn in a recession out of the ** largest metro areas in the United States. The Californian metro area received an overall score of **** percent, which was compiled after factors such as home price volatility and average home loan-to-value ratio were examined.

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 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

Due to increasing inflation rates, economic growth has been slow in several countries worldwide, and some risk falling into recession. When asked about this, ** percent of respondents in South Korea believed that the country's economy had fallen into recession, and ** percent of respondents in Turkey did the same. In fact, South Korea's gross domestic product (GDP) growth rate increased by *** percent in the third quarter of 2023. Inflation increased rapidly around the world through 2022 and 2023, before it started falling in some countries in 2024.

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：Corporate Bond Credit Spread在04-01-2025达1.268基点，相较于03-01-2025的1.114基点有所增长。FRB Recession Risk：Corporate Bond Credit Spread数据按月更新，01-01-1973至04-01-2025期间平均值为1.572基点，共628份观测结果。该数据的历史最高值出现于11-01-2008，达7.924基点，而历史最低值则出现于10-01-1978，为0.563基点。CEIC提供的FRB Recession Risk：Corporate Bond Credit Spread数据处于定期更新的状态，数据来源于Federal Reserve Board，数据归类于全球数据库的美国 – Table US.S090: FRB Recession Risk。

Abstract The literature, although point variables including lack of public safety, unemployment, corruption and the level of education as factors that influence the increase of entrepreneurial intention, does not investigate the effect of such variables through the existence of the endogenous relationship between economic growth and entrepreneurial intention. The objective of this study is to verify whether the economic recession influences entrepreneurial intention. The sample consisted of 60 234 individuals from 37 countries in 2009. Our results show that in scenarios with major economic downturns, people say they are more conducive to engage in entrepreneurial activities. Such result suggests that individuals seek in the economic crisis for business opportunities that may be motivated, possibly, by entrepreneurship by necessity. These evidences indicate that research on entrepreneurial intention should consider the economic situation of the analyzed environment.

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

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

FRB Recession Risk：Excess Bond Premium在04-01-2025达-0.105基点，相较于03-01-2025的-0.060基点有所下降。FRB Recession Risk：Excess Bond Premium数据按月更新，01-01-1973至04-01-2025期间平均值为-0.056基点，共628份观测结果。该数据的历史最高值出现于10-01-2008，达3.539基点，而历史最低值则出现于07-01-2003，为-1.026基点。CEIC提供的FRB Recession Risk：Excess Bond Premium数据处于定期更新的状态，数据来源于Federal Reserve Board，数据归类于全球数据库的美国 – 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.

NGT:TSX 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