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.

Consumer Confidence in China increased to 88 points in May from 87.80 points in April of 2025. This dataset provides - China Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Confidence Economic Expectations in the United States decreased to 80.20 in July from 83.90 in June of 2017. This dataset includes a chart with historical data for the United States Consumer Confidence Economic Expectations.

United States Consumer Confidence Index data was reported at 86.000 1985=100 in Apr 2025. This records a decrease from the previous number of 93.900 1985=100 for Mar 2025. United States Consumer Confidence Index data is updated monthly, averaging 98.300 1985=100 from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 144.700 1985=100 in May 2000 and a record low of 25.300 1985=100 in Feb 2009. United States Consumer Confidence Index data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H049: Consumer Confidence Index. [COVID-19-IMPACT]

Consumer Confidence in Japan increased to 34.50 points in June from 32.80 points in May of 2025. This dataset provides - Japan Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States - University of Michigan: Consumer Sentiment was 52.20000 Index 1966:Q1=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - University of Michigan: Consumer Sentiment reached a record high of 112.00000 in January of 2000 and a record low of 50.00000 in June of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - University of Michigan: Consumer Sentiment - last updated from the United States Federal Reserve on July of 2025.

This dataset provides values for CONSUMER CONFIDENCE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Key information about United States Consumer Confidence Growth

• United States Consumer Confidence dropped by 6.2 % in Feb 2025, compared with a decrease of 5.0 % in the previous month.
• US Consumer Confidence: YoY Change is updated monthly, available from Feb 1968 to Feb 2025, averaged at 1.2 %.
• The data reached an all-time high of 116.9 % in Dec 1975 and a record low of -66.9 % in Feb 2009.
• In the latest reports, Retail Sales of US grew 1.0 % YoY in May 2023.

In April 2020, the global consumer confidence index of ** countries worldwide dropped to **** following the outbreak of the COVID-19 pandemic. It then slowly increased until July 2021, when it reached an index score of ****. Global consumer confidence dropped in the latter half of 2022 following rising inflation rates, but has been increasing since November that year.

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.

Consumer Confidence in India decreased to 95.40 points in May from 95.50 points in March of 2025. This dataset provides - India Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The U.S. Economy on a Growth Trajectory, but Risks Loom

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

Consumer Confidence in Canada increased to 48.80 points in June from 48.10 points in May of 2025. This dataset provides - Canada Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The US Economy: A House of Cards?

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.

US Economy to Slow in June as Fed Tightens Monetary Policy

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

Consumer Confidence in the United Kingdom increased to -18 points in June from -20 points in May of 2025. This dataset provides the latest reported value for - United Kingdom Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Dow Jones U.S. Select Investment Services Index: Riding the Economic Tides?

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.

Bankruptcy Boom: The U.S. Economy in Turmoil

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

Consumer Confidence in Hong kong increased to 70.60 points in the fourth quarter of 2021 from 61.90 points in the third quarter of 2021. This dataset provides - Hong Kong Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Westpac MNI China Consumer Sentiment Index went up to 116.6 in December of 2016 from 114.9 in November, driven by an increase in the indices of current personal finances (+2.8 percent to 113.0, the highest since May 2014) and propensity to save. At the same time, consumers showed concerns about the 2017 outlook for jobs. This dataset provides - China Mni Consumer Sentiment- actual values, historical data, forecast, chart, statistics, economic calendar and news.