By November 2025, it is projected that there is a probability of 33.56 percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

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.

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

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.

In December 2024, the yield on a 10-year U.S. Treasury note was 4.39 percent, forecasted to decrease to reach 3.27 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.

According to projections by a range of economic institutions, the economy of the Euro currency area is forecast to grow by between 0.5 percent and 1.2 percent in 2024. The Eurozone saw slow growth in 2023, when it grew by 0.7 percent - albeit this was significantly better than many economic forecasts which predicted a recession in the EU in that year. Across all the forecasts included, growth is expected to pick up in 2025, when the Eurozone's economy is expected to grow between 1.4 and 1.8 percent.

For 2024, various sources estimate that the Swiss GDP will increae by varying percentages. This is due to the hoped recovery from the recession expected in 2024.

Predicted probabilities of Self-Rated health by recession experiences.

United States banking professionals believed in Q2 2022 that a Fed overcorrection was a probable cause for a recession. 51 percent of the respondents believed that the too fast and too highly increasing Fed rates would result in an economic recession. 25 percent of the respondents predicted that a recession would occur because of supply chain problems, while five percent mentioned the conflict in Eastern Europe as the main cause for a possible recession.

This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, the authors apply a mixed-frequency Markov-switching vector autoregressive (MF-MSVAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. The results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. The results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.

Graph and download economic data for NBER based Recession Indicators for the United States from the Period following the Peak through the Trough (USREC) from Dec 1854 to Feb 2025 about peak, trough, recession indicators, and USA.

In the context of predicting the term structure of interest rates, we explore the marginal predictive content of real-time macroeconomic diffusion indexes extracted from a data rich real-time data set, when used in dynamic Nelson-Siegel (NS) models of the variety discussed in Svensson (NBER technical report, 1994; NSS) and Diebold and Li (Journal of Econometrics, 2006, 130, 337-364; DNS). Our diffusion indexes are constructed using principal component analysis with both targeted and untargeted predictors, with targeting done using the lasso and elastic net. Our findings can be summarized as follows. First, the marginal predictive content of real-time diffusion indexes is significant for the preponderance of the individual models that we examine. The exception to this finding is the post Great Recession period. Second, forecast combinations that include only yield variables result in our most accurate predictions, for most sample periods and maturities. In this case, diffusion indexes do not have marginal predictive content for yields and do not seem to reflect unspanned risks. This points to the continuing usefulness of DNS and NSS models that are purely yield driven. Finally, we find that the use of fully revised macroeconomic data may have an important confounding effect upon results obtained when forecasting yields, as prior research has indicated that diffusion indexes are often useful for predicting yields when constructed using fully revised data, regardless of whether forecast combination is used, or not. Nevertheless, our findings also underscore the potential importance of using machine learning, data reduction, and shrinkage methods in contexts such as term structure modeling.

The Great Recession is a non-trivial test bed for the theory of economic voting, especially if its predictions are decomposed at the party level, as done in this article by analysing the electoral performances of parties competing in 89 national elections held in the 28 member states of the EU between 2003 and 2015. We acknowledge counterintuitively that prime ministers’ parties are able to exploit the relatively good state of the economy, while sharing the blame with their allies in times of crisis, counting on the lack of clarity in the attribution of responsibilities and deploying their heresthetic capacities. We further recognize that new parties, more than opposition ones, proportionally profited from the recession. Tough times magnify the alternation between left- and right-wing victories, without necessarily favouring the most radical parties, whereas the EU’s supposed responsibility in prolonging the crisis fuelled the success of Eurosceptic parties.

In 2023 the gross domestic product (GDP) of the United Kingdom grew by 0.1 percent and is expected to grow by 1.1 percent in 2024 and two percent in 2025. Growth is expected to slow down to 1.8 percent in 2026, and then 1.5 percent in 2027 and 2028. The sudden emergence of COVID-19 in 2020 and subsequent closure of large parts of the economy were the cause of the huge 9.4 percent contraction in 2020, with the economy recovering somewhat in 2021, when the economy grew by 7.6 percent. UK slips into recession in late 2023 In the last two quarters of 2023, the UK economy shrank by 0.1 percent in Q3 and by 0.3 percent in Q4, plunging the UK into recession for the first time since the COVID-19 pandemic. Even before this latest recession, however, the UK economy has been struggling with weak growth. In the eight quarters between 2022 and 2023, the economy grew in just half of them, falling in three, and stagnating in one. As the UK gears up for a likely general election in 2024, the economy has consistently been seen as one of the most important issues to people in Britain, ahead of health, immigration and the environment. As for which political party would handle the economy better, the ruling-Conservative party have trailed the Labour Party on this issue in polls since October 2022. High inflation persisting longer than expected One of the main factors that explains the UK's economic woes recently is rising prices. UK inflation accelerated sharply from late 2021 onwards, and reached a peak of 11.1 percent in October 2022. Unfortunately for UK residents, wage growth has only recently caught up with inflation, with wages in real terms falling throughout for twenty months between November 2021 and June 2023. By January 2024, inflation had fallen to the more modest rate of four percent, but getting inflation down to such levels came at a price. The Bank of England raised interest rates throughout 2022 and 2023, which certainly played a part in the UK's weak economic performance during that time.

Coefficients from linear regression models predicting changes (M2→M3) in perceived current financial strain, N = 2569.

According to a survey carried out among banking professionals in the third quarter of 2022, more than half of the bank leaders believed that the U.S. economy was already in a recession or would be by the end of 2022. 36 percent of the respondents expected a recession in the first half of 2023, while 11 percent predicted a recession in the second half of 2023.

The Gross Domestic Product (GDP) in Hong Kong expanded 0.80 percent in the fourth quarter of 2024 over the previous quarter. This dataset provides - Hong Kong GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Coefficients from linear regression models predicting changes (M2→M3) in future work uncertainty, N = 2569.

This metadata record describes observed and predicted baseflow recession characteristics for 300 streamflow gauges in the western United States and 282 streamflow gauges in the eastern United States. Specifically, this record describes (1) the streamflow gauge locations (west or east) in the United States (Location), (2) the U.S. Geological Survey streamflow gauge identification numbers (USGS_Site_Identifier), (3) observed regions of similar aquifer hydraulic properties (7 regions coded by color: blue, green, red, purple, grey, pink, and orange) by k-means clustering method (Observed_Class(k-means)), (4) predicted regions of similar aquifer hydraulic properties by random forest classification models (Predicted_Class(k-means)), (5) calculated long-term baseflow recession constant at streamflow gauges (Observed_a-long[ft^(-3/2)s^(-1/2)]), (6) predicted long-term baseflow recession constant by novel empirical and physical approach (Predicted_a-long(Novel)[ft^(-3/2)s^(-1/2)]), (7) pre ...