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 Recession Probability data was reported at 14.120 % in Oct 2019. This records a decrease from the previous number of 14.505 % for Sep 2019. United States Recession Probability data is updated monthly, averaging 7.668 % from Jan 1960 (Median) to Oct 2019, with 718 observations. The data reached an all-time high of 95.405 % in Dec 1981 and a record low of 0.080 % in Sep 1983. United States Recession Probability data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

Graph and download economic data for OECD based Recession Indicators for New Zealand from the Period following the Peak through the Trough (DISCONTINUED) (NZLRECD) from 1960-02-01 to 2017-11-30 about New Zealand, peak, trough, and recession indicators.

Germany's factory activity slump signals possible winter recession, highlighting manufacturing challenges and economic concerns.

This poll, fielded January 13-16, 2009, is a part of a continuing series of monthly surveys that solicits public opinion on the presidency and on a range of other political and social issues. A national sample of 1,079 adults was surveyed, including an oversample of 204 African Americans. Opinions were sought on how well George W. Bush handled his job as president, how Dick Cheney handled his job as vice president, and whether things in the country were going in the right direction. Respondents were asked their opinions about how they thought President George Bush would go down in history, how newly elected Barack Obama handled his presidential transition, the level of confidence they had in President Obama and Congress to make decisions for the country's future, the expectations they had for Obama's performance as president, whether he got off to a good start in dealing with the economy, and the confidence level they had that President Obama's economic program would improve the economy. Views were sought on the kind of priority the president and Congress should give several issues including the economy, the situation in Iran, in Israel, and in Afghanistan, the federal budget deficit, education, global warming, health care, immigration issues, the United States campaign against terrorism, and taxes. Respondents were also asked questions about and the kind of priority that should be given to items that could be included in the economic stimulus plan such as upgrading schools with new technology, computerizing American medical records, extending unemployment insurance and health care coverage, and putting a moratorium on home mortgage foreclosures. Several questions addressed race relations and asked such things as whether Blacks in the community receive equal treatment, whether respondents felt they were ever denied housing or a job because of their race, and whether they felt they had ever been stopped by the police because of their race. Additional topics covered included respondents' personal finances, the war in Iraq, the situation in Afghanistan, the United States military prison at Guantanamo Bay, the treatment of terrorist suspects, embryonic stem cell research, and race relations. Demographic variables include sex, age, race, education level, political party affiliation, political philosophy, religious preference, and household income.

A recession is due in the U.S. in 2023, according to a majority of macroeconomists in a June 2022 survey. Opinions varied, however, on when in 2023 this new recession could start exactly. Most respondents - ** percent - believed the economic downturn most likely start in the first half of 2023. Meanwhile, ** percent said that it would begin in the latter half of that year. Most Americans thought differently on this topic, believing that the country was already experiencing an economic recession in June 2022. The macroeconomists cited both geopolitical tensions and the increasing costs of energy as the main reasons why pressure would remain on U.S. inflation.

United States Recession Prob: Yield Curve: 3 Month Treasury Yield data was reported at 2.250 % in Oct 2018. This records an increase from the previous number of 2.130 % for Sep 2018. United States Recession Prob: Yield Curve: 3 Month Treasury Yield data is updated monthly, averaging 4.620 % from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 16.300 % in May 1981 and a record low of 0.010 % in Dec 2011. United States Recession Prob: Yield Curve: 3 Month Treasury Yield data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

From the Summer of 2007 until the end of 2009 (at least), the world was gripped by a series of economic crises commonly known as the Global Financial Crisis (2007-2008) and the Great Recession (2008-2009). The financial crisis was triggered by the collapse of the U.S. housing market, which caused panic on Wall Street, the center of global finance in New York. Due to the outsized nature of the U.S. economy compared to other countries and particularly the centrality of U.S. finance for the world economy, the crisis spread quickly to other countries, affecting most regions across the globe. By 2009, global GDP growth was in negative territory, with international credit markets frozen, international trade contracting, and tens of millions of workers being made unemployed.

Global similarities, global differences

Since the 1980s, the world economy had entered a period of integration and globalization. This process particularly accelerated after the collapse of the Soviet Union ended the Cold War (1947-1991). This was the period of the 'Washington Consensus', whereby the U.S. and international institutions such as the World Bank and IMF promoted policies of economic liberalization across the globe. This increasing interdependence and openness to the global economy meant that when the crisis hit in 2007, many countries experienced the same issues. This is particularly evident in the synchronization of the recessions in the most advanced economies of the G7. Nevertheless, the aggregate global GDP number masks the important regional differences which occurred during the recession. While the more advanced economies of North America, Western Europe, and Japan were all hit hard, along with countries who are reliant on them for trade or finance, large emerging economies such as India and China bucked this trend. In particular, China's huge fiscal stimulus in 2008-2009 likely did much to prevent the global economy from sliding further into a depression. In 2009, while the United States' GDP sank to -2.6 percent, China's GDP, as reported by national authorities, was almost 10 percent.

Several prominent economists have argued that existing DSGE models cannot properly account for the evolution of key macroeconomic variables during and following the recent Great Recession. We challenge this argument by showing that a standard DSGE model with financial frictions available prior to the recent crisis successfully predicts a sharp contraction in economic activity along with a protracted but relatively modest decline in inflation, following the rise in financial stress in 2008:IV. The model does so even though inflation remains very dependent on the evolution of economic activity and of monetary policy. (JEL E12, E31, E32, E37, E44, E52, G01)

Explore the surprising 1% decline in German industrial production in October, signaling ongoing struggles and potential recession in Europe's largest economy.

The UK economy shrank by 0.1 percent in May 2025 after shrinking by 0.3 percent in April. Since a huge decline in GDP in April 2020, the UK economy has gradually recovered and is now around 4.4 percent larger than it was before the COVID-19 pandemic. After the initial recovery from the pandemic, however, the UK economy has effectively flatlined, fluctuating between low growth and small contractions since January 2022. Labour banking on growth to turn around fortunes in 2025 In February 2025, just over half a year after winning the last general election, the approval rating for the new Labour government fell to a low of -48 percent. Furthermore, the Prime Minister, Keir Starmer was not only less popular than the new Conservative leader, Kemi Badenoch, but also the leader of the Reform Party, Nigel Farage, whose party have surged in opinion polls recently. This remarkable decline in popularity for the new government is, in some part, due to a deliberate policy of making tough decisions early. Arguably, the most damaging of these policies was the withdrawal of the winter fuel allowance for some pensioners, although other factors such as a controversy about gifts and donations also hurt the government. While Labour aims to restore the UK's economic and political credibility in the long term, they will certainly hope for some good economic news sooner rather than later. Economy bounces back in 2024 after ending 2023 in recession Due to two consecutive quarters of negative economic growth, in late 2023 the UK economy ended the year in recession. After not growing at all in the second quarter of 2023, UK GDP fell by 0.1 percent in the third quarter, and then by 0.3 percent in the last quarter. For the whole of 2023, the economy grew by 0.4 percent compared to 2022, and for 2024 is forecast to have grown by 1.1 percent. During the first two quarters of 2024, UK GDP grew by 0.7 percent, and 0.4 percent, with this relatively strong growth followed by zero percent growth in the third quarter of the year. Although the economy had started to grow again by the time of the 2024 general election, this was not enough to save the Conservative government at the time. Despite usually seen as the best party for handling the economy, the Conservative's economic competency was behind that of Labour on the eve of the 2024 election.

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

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.

TA: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

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.

if the stock market goes down during a recession, you should sell all of your investments to minimize your losses.

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.

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 trajectory of new business applications and transitions to employer businesses differ markedly during the Great Recession and COVID-19 Recession. Both applications and transitions to employer startups decreased slowly but persistently in the post-Lehman crisis period of the Great Recession. In contrast, during the COVID-19 Recession new applications initially declined but have since sharply rebounded, resulting in a surge in applications during 2020. Projected transitions to employer businesses also rise but this is dampened by a change in the composition of applications in 2020 towards applications that are more likely to be nonemployers.

Goldman Sachs recommends gold as a strategic asset amid recession fears, with potential for prices to exceed $3,700 due to economic uncertainties and central bank diversification.

Reservoir Bank Recession in The Connecticut River Basin, New Hampshire and Vermont. Bank erosion is a concern along some of the reservoirs because it causes loss of land, disrupts shoreline habitats, destroys cultural resources sites, and introduces sediment into the reservoirs which can subsequently degrade them for their intended uses. The objective of this study was to evaluate recession at selected sites so as to provide insights into the likely causes of erosion and recession at other reservoirs within northern New England. For more information on this resource and accessibility options please see the links provided. Paper in the Vermont Geological Society quarterly newsletter - Green Mountain Geologist, Winter 1991. Volume 17, Number 4, page 53 of 77 in PDF.

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.

Understanding the Dynamics and Implications of a Housing Market 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

Gold prices fell by 3.58% on Monday due to global tariff concerns, yet remain up 16.77% since January amid economic uncertainty.