This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Sep 2025 about recession indicators, academic data, and USA.

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.

The non-durable materials market took almost three and a half years to recover its performance levels from the effects caused by the great recession (between 2007 and 2009). Other industries, such as construction, and metals and mining, have still not returned to their pre-recession peak performances (as of May 2020).

Though labor market statistics are often reported and discussed at the national level, conditions can vary quite a bit across individual states. We explore differences in these conditions before and after the Great Recession using a ratio of the number of unemployed workers to job vacancies. We show that the intensity of the adverse effects of the recession and the strength of the recovery varied geographically at all points in the process. We also demonstrate that wage growth is delayed until the ratio of unemployed workers to job vacancies returns to prerecession levels.

The AI Sensor market is poised for explosive growth, demonstrating remarkable resilience even amidst a global recession. Driven by the urgent need for automation, efficiency, and cost optimization across industries, the demand for intelligent sensors is accelerating. While economic uncertainty may cause short-term hesitations in capital expenditure, the long-term strategic value of AI-driven data analysis in predictive maintenance, quality control, and autonomous systems positions the market for substantial expansion. Sectors such as manufacturing, automotive, healthcare, and logistics are leading this adoption wave. The market's trajectory is fueled by advancements in edge computing, IoT proliferation, and increasingly sophisticated machine learning algorithms, which together unlock unprecedented operational insights and capabilities, making AI sensors a critical investment for future-proofing businesses. Key strategic insights from our comprehensive analysis reveal:

Despite recessionary pressures, the market is projected to grow at an exceptional CAGR of 38.6%, as businesses prioritize long-term efficiency and automation investments over short-term discretionary spending.
The push for operational resilience is shifting focus towards high-ROI applications like predictive maintenance and energy management, which offer clear and rapid cost-saving benefits in a challenging economic climate.
North America and Asia Pacific are the dominant regions, driven by strong technology ecosystems and massive manufacturing bases, respectively, creating a competitive and innovative landscape for AI sensor development and deployment.

Global Market Overview & Dynamics of AI Sensor Market with Recession Market Analysis The global AI Sensor market is on a path of transformative growth, fundamentally reshaping how industries collect, process, and act on data. This expansion is propelled by the convergence of advanced sensor technology, powerful edge computing, and sophisticated AI algorithms. Even with the backdrop of a global recession, the market's momentum is sustained by an intensified focus on automation and operational efficiency as companies seek to reduce costs and enhance productivity. AI sensors are becoming integral to diverse applications, from industrial IoT and autonomous vehicles to smart cities and personalized healthcare, creating a dynamic and highly competitive environment. The ability of these sensors to provide real-time, actionable intelligence at the source is the core value proposition driving their widespread adoption. Global AI Sensor Market with Recession Market Drivers

Imperative for Automation and Cost Reduction: During a recession, businesses aggressively seek to reduce operational expenditures and enhance productivity. AI sensors enable automation in manufacturing, logistics, and quality control, directly addressing these needs by minimizing labor costs, reducing errors, and optimizing resource utilization.
Proliferation of IoT and Edge Computing: The expanding Internet of Things (IoT) ecosystem generates massive volumes of data. AI sensors with edge computing capabilities can process this data locally, reducing latency, lowering bandwidth costs, and enabling real-time decision-making, which is critical for applications like autonomous systems and smart infrastructure.
Advancements in AI and Sensor Technology: Continuous improvements in machine learning algorithms, coupled with the miniaturization and cost reduction of high-performance sensors (like LiDAR, radar, and image sensors), are making sophisticated AI-powered sensing more accessible and effective for a broader range of applications.

Global AI Sensor Market with Recession Market Trends

Surge in Predictive Maintenance Applications: Industries are increasingly adopting AI sensors to monitor equipment health in real-time. By predicting failures before they occur, companies can minimize costly unplanned downtime and transition from reactive to proactive maintenance strategies, a trend that gains significant traction during economic downturns.
Integration into Autonomous Vehicles and ADAS: The automotive sector is a key growth area, with AI sensors forming the sensory backbone of Advanced Driver-Assistance Systems (ADAS) and fully autonomous vehicles. The fusion of data from cameras, radar, and LiDAR, processed by onboard AI, is critical for safe and reliable navigation.
Rise of TinyML and On-Device AI: The trend ...

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.

Countries with flexible institutions and labor market policies, like the U.S., experienced increases in unemployment over the course of the Great Recession, while those with relatively rigid institutions and strict labor market policies fared better.

View monthly updates and historical trends for US Recession Probability. from United States. Source: Federal Reserve Bank of New York. Track economic data…

Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q1 2025 about recession indicators, GDP, and USA.

Article on the impact of the recession on the labour market Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Labour Market and Recession

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

Both consumer and government spending have continued to rise despite interest rate hikes by the Federal Reserve, but recession fears still loom.

Between ************ and *********, global recession fear went through periods of sharp increases three times. First, in the summer of 2019, due to an escalation in U.S.-China relations and a recession signal being flashed by the bond market. The second peak of worldwide recession fear took place in **********, as a result of the alarming jump in the rate of COVID-19 cases. The fear of recession started to increase sharply again in *************, as the conflict between Russia and Ukraine escalated.

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IBISWorld examines the potentially significant effects of a global recession on domestic industries, businesses and consumers.

With the collapse of the U.S. housing market and the subsequent financial crisis on Wall Street in 2007 and 2008, economies across the globe began to enter into deep recessions. What had started out as a crisis centered on the United States quickly became global in nature, as it became apparent that not only had the economies of other advanced countries (grouped together as the G7) become intimately tied to the U.S. financial system, but that many of them had experienced housing and asset price bubbles similar to that in the U.S.. The United Kingdom had experienced a huge inflation of housing prices since the 1990s, while Eurozone members (such as Germany, France and Italy) had financial sectors which had become involved in reckless lending to economies on the periphery of the EU, such as Greece, Ireland and Portugal. Other countries, such as Japan, were hit heavily due their export-led growth models which suffered from the decline in international trade. Unemployment during the Great Recession As business and consumer confidence crashed, credit markets froze, and international trade contracted, the unemployment rate in the most advanced economies shot up. While four to five percent is generally considered to be a healthy unemployment rate, nearing full employment in the economy (when any remaining unemployment is not related to a lack of consumer demand), many of these countries experienced rates at least double that, with unemployment in the United States peaking at almost 10 percent in 2010. In large countries, unemployment rates of this level meant millions or tens of millions of people being out of work, which led to political pressures to stimulate economies and create jobs. By 2012, many of these countries were seeing declining unemployment rates, however, in France and Italy rates of joblessness continued to increase as the Euro crisis took hold. These countries suffered from having a monetary policy which was too tight for their economies (due to the ECB controlling interest rates) and fiscal policy which was constrained by EU debt rules. Left with the option of deregulating their labor markets and pursuing austerity policies, their unemployment rates remained over 10 percent well into the 2010s. Differences in labor markets The differences in unemployment rates at the peak of the crisis (2009-2010) reflect not only the differences in how economies were affected by the downturn, but also the differing labor market institutions and programs in the various countries. Countries with more 'liberalized' labor markets, such as the United States and United Kingdom experienced sharp jumps in their unemployment rate due to the ease at which employers can lay off workers in these countries. When the crisis subsided in these countries, however, their unemployment rates quickly began to drop below those of the other countries, due to their more dynamic labor markets which make it easier to hire workers when the economy is doing well. On the other hand, countries with more 'coordinated' labor market institutions, such as Germany and Japan, experiences lower rates of unemployment during the crisis, as programs such as short-time work, job sharing, and wage restraint agreements were used to keep workers in their jobs. While these countries are less likely to experience spikes in unemployment during crises, the highly regulated nature of their labor markets mean that they are slower to add jobs during periods of economic prosperity.

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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

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.

MMI 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