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.

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

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

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

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.

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

The UK economy grew by 0.4 percent in May 2025 after shrinking by 0.1 percent in May. 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.

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

Formaat: PDF Omvang: 70Kb

This article was published on the Guardian website at 19.00 BST on Tuesday 16 June 2009. It was last modified at 13.43 BST on Tuesday 19 August 2014. Online beschikbaar: http://www.theguardian.com/commentisfree/2009/jun/16/deflation-double-dip-recession-inflation/print [01-12-2014] © 2014 Guardian News and Media Limited or its affiliated companies. All rights reserved.

The present study focuses on the fluctuation of sentiment in economic terminology to observe semantic changes in restricted diachrony. Our study examines the evolution of the target term ‘inflation’ in the business section of quality news and the impact of the Great Recession. This is carried out through the application of quantitative and qualitative methods: Sentiment Analysis, Usage Fluctuation Analysis, Corpus Linguistics, and Discourse Analysis. From the diachronic Great Recession News Corpus that covers the 2007–2015 period, we extracted sentences containing the term ‘inflation’. Several facts are evidenced: (i) terms become event words given the increase in their frequency of use due to the unfolding of relevant crisis events, and (ii) there are statistically significant culturally motivated changes in the form of emergent collocations with sentiment-laden words with a lower level of domain-specificity.

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.

NGT: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

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

Gold and copper prices have declined due to global market sell-offs and geopolitical tensions, with gold slipping below $3,000 an ounce.

Goldman Sachs forecasts a decline in oil prices into 2026, influenced by recession risks and increased OPEC+ supply, with potential for Brent prices to fall into the $40 range.

This week is crucial for global markets with key events like FOMC meetings, PCE data releases, and major earnings reports, potentially influencing market trends and investor strategies.

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

This poll, fielded August 27-31, 2009, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked whether they approved of the way Barack Obama was handling the presidency, foreign policy, the situation in Afghanistan, health care, and the economy. Respondents were asked if they thought things in the country were on the right track, their rating of the national economy, and whether they thought the economy would get better. Respondents were also asked questions about the economic recession, including how long they thought it would last, the advisability of the federal government spending money to stimulate the national economy, whether it was acceptable to raise the deficit to create jobs and stimulate growth, and whether the federal budget deficit affected the respondent's family's financial situation. Several questions addressed health care, including whether respondents thought our health care system worked well, whether Medicare worked well, and whether the government would do a better job than private health care companies in keeping health care costs down and providing medical coverage. Respondents were also asked their opinions on the health insurance industry, whether they believed in the possibility of expanding health care coverage without increasing budget deficits or taxes on the middle class, whether Barack Obama or the Republicans in Congress had better ideas about reforming the health care system, and whether they understood the health care reforms Congress was considering. Information was collected on how respondents thought health care reforms under consideration in Congress would affect the middle class, senior citizens, small businesses, the respondent personally, their health care costs, and the quality of health care. Additional topics that were covered included the pullout of troops from Iraq, major credit cards, credit card debt, how the federal government should use taxpayer's money, how to handle the deficit, personal finances, the best way to discourage obesity, and job security. Demographic variables include sex, age, race, marital status, education level, household income, political party affiliation, political philosophy, perceived social class, religious preference, and voter registration status and participation history.

Explore the stability of gold prices amidst economic uncertainty in the U.S., despite slight dips and fluctuating market conditions.

Eu Glucose Market 2019: the glucose market size in the European Union amounted to $2.7B in 2018, declining by -2.9%.

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