Consumer Confidence in the United States increased to 60.70 points in June from 52.20 points in May of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Consumer Confidence: Current Index data was reported at 38.471 Index in Jan 2023. This records an increase from the previous number of 38.436 Index for Dec 2022. United States Consumer Confidence: Current Index data is updated monthly, averaging 46.474 Index from Jan 2002 (Median) to Jan 2023, with 253 observations. The data reached an all-time high of 57.063 Index in May 2018 and a record low of 27.208 Index in Mar 2009. United States Consumer Confidence: Current Index data remains active status in CEIC and is reported by Ipsos Group S.A.. The data is categorized under Global Database’s United States – Table US.IPSOS: Consumer Confidence Survey.

Consumer Confidence In the Euro Area decreased to -15.30 points in June from -15.10 points in May of 2025. This dataset provides the latest reported value for - Euro Area Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. This type of information is essential for forecasting changes in aggregate consumer behavior. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. Each monthly survey probes a different aspect of consumer confidence. Open-ended questions are asked concerning evaluations and expectations about personal finances, employment, price changes, and the national business situation. Additional questions probe buying intentions for automobiles and computers, and the respondent's appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey were respondents' types of savings and financial investments, family income, respondents' knowledge and use of the Internet, use of a PC at home and in the office, ownership, rental, and use of automobiles, and vote cast in the last presidential election. Demographic information includes ethnic origin, sex, age, marital status, and education.

This study is part of a time-series collection of national surveys fielded continuously since 1948. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. The ANES 1962 Time Series Study is a traditional time series study, conducted face-to-face after the congressional election. The data were collected as part of the Survey Research Center Economic Behavior Program's Fall Omnibus Survey, which was designed to measure consumer confidence and optimism but also included questions in other areas such as political behavior and political attitudes. The questionnaire used served both the 1962 ANES and the Fall Omnibus, but the 1962 ANES excluded questions that were specifically gathered for the EBP survey alone. In addition to content on electoral participation, voting behavior, and public opinion, the 1962 ANES includes items on partisanship, government enforcement of school integration, and financial and business conditions.

Abstract copyright UK Data Service and data collection copyright owner.

Gallup political polls are conducted on a regular basis several times each month by Social Surveys (Gallup Poll) Ltd. The Archive holds the data from these polls from 1958 to the 1990s, We expect to update our stock regularly. The Archive can also supply the data from a series of polls from November 1938 to September 1946, complete with SPSS set-ups for each study.
A CD-ROM product Database of Selected British Gallup Opinion Polls (SN:3803) is also available from the Data Archive. Further information is available on request.
Main Topics:
Variables
A wide variety of political, social and economic subjects are covered, including among others:
Satisfaction with: Government's performance, Opposition policies, the Prime Minister, the Leader of the Opposition.
Voting record and intention.
Political parties and individuals.
Specific political issues and current problems.
Consumer confidence.
Social concerns, such as law and crime, nationalisation, emigration, etc.
Respondents are classified by age, sex, marital status, socio-economic group, employment status and occupation, self-assessed social class, trade union membership, size of household, number of children, terminal education age, car ownership.
More information on these surveys is provided in the 'Gallup Political Index', available in many libraries. The Index provides specific information on a selection of the polls for which we hold the data. For more information on the holdings of the Archive, please contact The User Service Section, Data Archive.

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.

YouGov (YOU) Stock Forecast: Polling the Market for Growth Potential

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): This study is part of a time-series collection of national surveys fielded continuously since 1952. The American National Election Studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. This data collection is a subset of a larger survey conducted by the Economic Behavior Program of the Survey Research Center (AMERICAN NATIONAL ELECTION STUDY, 1960 [ICPSR 7216]) and includes only a limited number of items pertaining to political behavior, with the major focus on attitudinal questions designed to measure consumer optimism and confidence. All persons of voting age living in private households in the United States. National multistage area probability sample. 2014-06-25 Internal records were updated Funding insitution(s): National Science Foundation. telephone interview

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.

GFL GFL Environmental Inc. Subordinate voting shares no par value

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

Ipsos Global @dvisor wave 21 was conducted on May 9 and May 20, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, BE: Threat Index, CM: Global Attitudes Toward Opinion Polling, CP: Osama bin Laden.

Public opinion data on United States and California macropartisanship, or mass party identification, from 1969-2010. Based on Gallup and Field Poll results, respectively. Also includes US-level measures of consumer sentiment, presidential approval, presidential party, and indicator for presidential administration. Additional data are from the Texas Poll from 1990-1998 to capture macropartisanship in Texas.

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.

Brookfield Business (BBUC) Shares: Exchangeable, Subordinate, Voting?

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