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.

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

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.

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.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Eddy Billard on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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

Economic Policy Uncertainty and Recession Probabilities, Academic Data dataset contains the series for the following categories Recession Probabilities, Economic Policy Uncertainty

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.

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

Abstract (en): Some analysts and economists recently warned that the United States economy faces a much higher risk of recession should the price of oil rise to $100 per barrel or more. In February 2008, spot crude oil prices closed above $100 per barrel for the first time ever, and since then they have climbed even higher. Meanwhile, according to some surveys of economists, it is highly probable that a recession began in the United States in late 2007 or early 2008. Although the findings in this paper are consistent with the view that the United States economy has become much less sensitive to large changes in oil prices, a simple forecasting exercise using Hamilton's model augmented with the first principal component of 85 macroeconomic variables reveals that a permanent increase in the price of crude oil to $150 per barrel by the end of 2008 could have a significant negative effect on the growth rate of real gross domestic product in the short run. Moreover, the model also predicts that such an increase in oil prices would produce much higher overall and core inflation rates in 2009 than most policymakers expect. A zipped package contains a programming syntax file (text format) and a Microsoft Excel file, which contains the data, tables, and corresponding figures used in the article.These data are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigators if further information is desired.

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.

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.

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

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

Barsky and Sims (2012, AER) demonstrated, via indirect inference, that confidence innovations can be viewed as noisy signals about medium-term economic growth. They highlighted that the connection between confidence and subsequent activity, such as consumption and output, is primarily driven by news shocks about the future. We expand upon their research by incorporating the Great Recession and ZLB episodes, during which animal spirits have a greater potential to influence economic activity. Nevertheless, we confirm the main finding of Barsky and Sims (2012) that this relationship is predominantly driven by news about the future rather than animal spirits.

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

Oil prices reach a four-year low due to U.S.-China trade tensions, impacting global commodities and raising recession fears.

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.

CDT 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.

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