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.

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

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 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

We use the yield curve to predict future GDP growth and recession probabilities. The spread between short- and long-term rates typically correlates with economic growth. Predications are calculated using a model developed by the Federal Reserve Bank of Cleveland. Released monthly.

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

According to Cognitive Market Research, the global AI Sensor Market with Recession Market size is USD 2.8 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 38.6% from 2024 to 2031. Market Dynamics of AI Sensor Market with Recession Market

Key Drivers for AI Sensor Market with Recession Market

Advancements in AI and Machine Learning: Rapid advances in artificial intelligence and machine learning are boosting the use of Al sensors. Algorithms are getting increasingly sophisticated and capable of handling complicated data from sensors, enabling real-time decision-making and predictive analytics. These developments allow Al sensors to detect patterns, anomalies, and trends in data streams, making them useful in applications such as picture recognition, natural language processing, and predictive maintenance. For instance, in manufacturing, Al sensors may detect faults in real time, improving quality control and lowering waste. Al sensors also improve the capability of autonomous systems and robots. They can perceive their surroundings, adjust to changing circumstances, and make sound decisions. This is especially crucial in industries like agriculture, where autonomous drones equipped with Al sensors can check crop health, detect pest infestations, and optimize pesticide use. Security and Surveillance applications

Key Restraints for AI Sensor Market with Recession Market

Capital Spending Delays in Price-Sensitive Sectors: Businesses in a variety of sectors, including retail, consumer electronics, and the automobile industry, frequently postpone or abandon capital-intensive initiatives and technological advancements during recessions. This has a direct impact on the use of AI sensors in consumer electronics, smart factories, and new goods, momentarily reducing market expansion.

Semiconductor shortages and supply chain disruptions: Complex semiconductor components are necessary for AI sensors, and supply chain bottlenecks are frequently made worse by global economic downturns. Delays in shipping, reduced manufacturing capacity, and geopolitical unrest can all affect sensor production and lengthen lead times, making it more difficult for industries to deploy sensors on time.

Key Trends for AI Sensor Market with Recession Market

Transition to Low-Cost Advanced AI Sensors: Industries are turning to edge AI sensors that analyze data locally in order to deal with financial restrictions. This eliminates the need for expensive cloud infrastructure and latency problems. Due to their simplicity of deployment and reduced total cost of ownership, small, energy-efficient sensors with on-chip AI are becoming more and more popular. Growing Utilization in Energy Efficiency and Predictive Maintenance: Operational efficiency is a top priority for financially stressed organizations, and AI sensors are essential for energy optimization and predictive maintenance. Industrial equipment with sensors built in can anticipate malfunctions, prolong the life of machinery, and use less electricity, all of which can result in quantifiable cost savings during recessions. Introduction of the AI Sensor Market with Recession Market

Al sensors are also improving the capabilities of autonomous systems and robots. They can perceive their surroundings, adjust to changing conditions, and make sound decisions. This is especially crucial in industries like agriculture, where autonomous drones equipped with Al sensors can check crop health, detect pest infestations, and optimize pesticide use. Also, increased demand for life-saving healthcare equipment and self-driving capabilities in new electric vehicles are expected to fuel growth. The global shift towards digitization is expected to boost growth even further.

Inspired by:

Modeling and predicting U.S. recessions using machine learning techniques

As variáveis do FRED-MD como preditivas e a USREC como alvo (período de 1979-2019)

Diversos Modelos: probit, logit, LDA, árvores Naive-Bayes Algumas variáveis tiveram que ser transformadas em mensais (interpolação cúbica)

128 varibles. Grupos: Output and Income Labor Market Consumption and Orders Orders and Inventories Money and Credit Interest Rates and Exchange Rates Prices Stock Market

In 2024, the gross domestic product (GDP) of the United Kingdom grew by *** percent and is expected to grow by just *** percent in 2025 and by *** percent in 2026. Growth is expected to slow down to *** percent in 2027, and then grow by ***, and *** percent in 2027 and 2028 respectively. The sudden emergence of COVID-19 in 2020 and subsequent closure of large parts of the economy were the cause of the huge *** percent contraction in 2020, with the economy recovering somewhat in 2021, when the economy grew by *** percent. UK growth downgraded in 2025 Although the economy is still expected to grow in 2025, the *** percent growth anticipated in this forecast has been halved from *** percent in October 2024. Increased geopolitical uncertainty as well as the impact of American tariffs on the global economy are some of the main reasons for this mark down. The UK's inflation rate for 2025 has also been revised, with an annual rate of *** percent predicated, up from *** percent in the last forecast. Unemployment is also anticipated to be higher than initially thought, with the annual unemployment rate likely to be *** percent instead of *** percent. Long-term growth problems In the last two quarters of 2023, the UK economy shrank by *** percent in Q3 and by *** percent in Q4, plunging the UK into recession for the first time since the COVID-19 pandemic. Even before that last recession, however, the UK economy has been struggling with weak growth. Although growth since the pandemic has been noticeably sluggish, there has been a clear long-term trend of declining growth rates. The economy has consistently been seen as one of the most important issues to people in Britain, ahead of health, immigration and the environment. Achieving strong levels of economic growth is one of the main aims of the Labour government elected in 2024, although after almost one year in power it has so far proven elusive.

Gaps between output and employment growth are often attributed to transitional phases by which the economy adjusts to shifts in the rate of trend productivity growth. Nevertheless, cyclical factors can also drive a wedge between output and employment growth. This article shows that one measure of cyclical dynamics--the expected output loss associated with a recession--helps predict the gap between output and employment growth in the coming four quarters. This measure of the output loss associated with a recession can take unexpected twists and turns as the recovery unfolds. The empirical results in this paper support the proposition that a weaker-than-expected rebound in the economy can partially mute employment growth for a time relative to output growth.

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.

MGRM Stock: Next Healthcare Disruptor or Destined for Downturn?

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

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.

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 repository hosts the supplementary materials associated with the paper:
> Delforge, D., Muñoz-Carpena, R., Van Camp, M. Vanclooster, M. (2020), A parsimonious empirical approach to streamflow recession analysis and forecasting (accepted at Water Resources Research - 29-01-2020).

This data set contains streamflow and recession data, a python code file and a Jupyter notebook illustrating how to apply the EDM-Simplex method to forecast the recession, and the outputs of the global sensitivity analysis. All files are documented in the readme.md Markdown files.

Streamflow data were obtained from the Aqualim portal (http://aqualim.environnement.wallonie.be/) of the "Service Public de Wallonie" and shared with their kind permission. This work is part of a Ph.D. supported by a FRIA grant from the Fund for Scientific Research (FSR-FNRS, Belgium). The authors acknowledge University of Florida Research Computing for providing computational resources and support that have contributed to the research results stored in this repository. URL: http://researchcomputing.ufl.edu.

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US Residential Construction Market Size 2025-2029

The US residential construction market size is forecast to increase by USD 242.9 million at a CAGR of 4.5% between 2024 and 2029.

The Residential Construction Market in the US is experiencing significant growth driven by increasing household formation rates and a rising focus on sustainability in new projects. According to the latest data, household formation is projected to continue growing at a steady pace, fueling the demand for new residential units. This trend is particularly evident in urban areas, where population growth and limited space for new development are driving up demand. Meanwhile, the emphasis on sustainability in residential construction is transforming the market landscape. With consumers increasingly prioritizing energy efficiency and eco-friendly features in their homes, builders and developers are responding by incorporating green technologies and sustainable materials into their projects.
This shift not only appeals to environmentally-conscious consumers but also offers long-term cost savings and regulatory compliance benefits. However, the market is not without challenges. Skilled labor shortages continue to pose a significant hurdle for large-scale residential real estate projects. The ongoing shortage of skilled laborers, including carpenters, electricians, and plumbers, is driving up labor costs and delaying project timelines. To mitigate this challenge, some builders are exploring alternative solutions, such as modular construction and automation, to streamline their operations and reduce their reliance on traditional labor sources. The Residential Construction Market in the US presents significant opportunities for companies seeking to capitalize on the growing demand for new housing units and the shift towards sustainability.
However, navigating the challenges of labor shortages and rising costs will require innovative solutions and strategic planning. By staying informed of market trends and adapting to evolving consumer preferences, companies can effectively position themselves for success in this dynamic market.

What will be the size of the US Residential Construction Market during the forecast period?

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The residential construction market in the United States continues to exhibit dynamic activity, driven by various economic factors. Housing supply remains a key focus, with ongoing discussions surrounding the affordable housing trend and efforts to increase inventory, particularly for single-family homes and new constructions. Mortgage and federal funds rates have an impact on residential investment, with fluctuations influencing buyer decisions and construction costs. The labor market plays a crucial role, as workforce availability and wages affect both housing starts and cancellation rates. Inflation and interest rates, monitored closely by the Federal Reserve, also shape the market's direction. Recession risks and economic conditions influence construction spending across various sectors, including multifamily and single-family homes.
Federal programs, such as housing choice vouchers and fair housing initiatives, continue to support home buyers and promote equitable housing opportunities. Building permits and housing starts serve as essential indicators of market health and future growth, with some sectors experiencing double-digit growth. Overall, the residential construction market in the US remains a significant economic driver, shaped by a complex interplay of economic, demographic, and policy factors.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Apartments and condominiums
  Luxury Homes
  Other types


Type

  New construction
  Renovation


Application

  Single family
  Multi-family


Construction Material

  Wood-framed
  Concrete
  Steel
  Modular/Prefabricated


Geography

  US

By Product Insights

The apartments and condominiums segment is estimated to witness significant growth during the forecast period.

The residential construction market in the US is experiencing growth in both the apartment and condominium sectors, driven by the increasing trend toward urbanization and changing lifestyle preferences. Apartments, typically owned by property management companies, and condominiums, with individually owned units within a larger complex, contribute significantly to the market. The Federal Reserve's influence on the economy through the federal funds rate and mortgage rates impacts borrowing rates and home construction activity. The affordability of housing, particularly for younger generations, is a concern due to factors such as inflation, labor market conditions, and savings

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 ...

The Gingival Recession Treatment Market is projected to reach $674.52 million by 2033, exhibiting a CAGR of 4.99% during the forecast period of 2025-2033. The growth is attributed to factors such as the rising prevalence of periodontal disease, increasing awareness about gingival recession, and technological advancements in treatment methods. Additionally, the growing adoption of minimally invasive procedures, such as laser therapy and guided tissue regeneration, is fueling market expansion. The market is segmented based on treatment type, material used, end user, and procedure complexity. Surgical treatment holds the largest market share due to the effectiveness and durability of the procedures. Non-surgical treatments, however, are gaining popularity as they are less invasive and offer faster recovery times. Collagen-based materials dominate the market due to their biocompatible and regenerative properties. Dental clinics account for the majority of the market share, while home care is expected to witness significant growth in the coming years due to the increasing availability of over-the-counter treatments. Key players in the market include Coltene, MediDent Supplies, and 3M. Recent developments include: In the Gingival Recession Treatment Market, recent developments showcase a growing emphasis on innovative solutions to address the gingival recession. Companies like Coltene and MediDent Supplies are investing in advanced technologies, aiming to enhance treatment efficacy and patient outcomes. The demand for bioactive materials and minimally invasive procedures is driving growth, with key players like 3M and GC Corporation expanding their product lines to include sophisticated regenerative materials. Current affairs indicate a surge in market valuation as awareness about oral health and aesthetics rises among consumers, promoting the market's expansion. Notable companies such as Patterson Companies and Ultradent Products are actively participating in this growth by launching new products designed for gingival recession treatment. Furthermore, merger and acquisition activities have been noted, with companies like Danaher and Dentsply Sirona exploring strategic partnerships to strengthen their market position and broaden their offerings. This collaborative approach is indicative of a competitive landscape where innovation and market relevance are crucial for success. The market’s dynamics reflect an increasingly interconnected environment where companies leverage advancements in technology and consumer healthcare trends to navigate evolving patient needs effectively.. Key drivers for this market are: Growing geriatric population, Increasing awareness of oral health; Advancements in regenerative medicine; Rising demand for minimally invasive procedures; Expanding dental tourism industry. Potential restraints include: Increasing prevalence of periodontal diseases, Growing awareness of oral health; Advances in treatment technologies; Rising aesthetic dentistry demand; Expanding geriatric population.

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.