Consumer Confidence in the United States increased to 61.70 points in July from 60.70 points in June 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.

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.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Consumer Spending And Sentiment (EMVMACROCONSUME) from Jan 1985 to Jun 2025 about volatility, uncertainty, equity, PCE, consumption expenditures, consumption, personal, and USA.

In July 2025, consumer confidence in Japan reached **** index points. The index is based on a representative survey of Japanese households (excluding one-person households). They are asked to give their assessment of the areas of quality of life, income growth, employment and propensity to durable goods. From the responses, the overall index is calculated; seasonal adjustment via X-12-ARIMA. An index value above 50 indicates a positive mood of consumers, a reading below 50 points to a negative assessment.

Consumer Confidence In the Euro Area increased to -14.70 points in July from -15.30 points in June 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.

Gallup's Economic Confidence Index combines the responses of Gallup's Economic Conditions and Economic Outlook measures. Daily results are based on telephone interviews with approximately 1,500 national adults; Margin of error is ±3 percentage points.

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.

Madison Square Garden Entertainment (MSGE) : A Rollercoaster Ride Ahead?

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 is measured through regular surveys of consumer expectations because it is seen as a useful predictor of consumption. However, substantial seasonality and shocks in consumer confidence suggest that not just economic expectations are being measured. One possibility is that events occurring when expectations are polled - such as major news or weather extremes - affect general mood, or perhaps specific emotions, and thus influence the surveyed responses. The research investigated the hypothesis that events influence affect, that, in turn, influences economic expectations and subsequent consumption.

The hypothesis was tested both through retrospective analyses of the effects of news events on consumer confidence using secondary data, and five empirical studies examining relationships between news, mood, consumer expectations and consumption decisions occurring both at the current time, and in the future. The basic design of the empirical studies was to manipulate mood or specific emotion, then use questionnaires to measure the influence of events and economic (and other) expectations. A couple of the studies relied on naturally occurring mood - which were measured using rating scales - rather than attempting to manipulate it. In one study (designed to investigate the effect of mood and expectations on consumption) one of the dependent variables was the participants' choice between products they wished to have as a gift.

Economic Optimism Index in Brazil increased to 86.70 points in July from 85.90 points in June of 2025. This dataset provides - Brazil Economic Optimism Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Replication data: Assessing the Relationship between Economic News Coverage and Mass Economic Attitudes. Monthly data January 1980-April 2014 on consumer sentiment, tone of media coverage, economic statistics.

Consumer Confidence in Japan decreased to 33.70 points in July from 34.50 points in June of 2025. This dataset provides - Japan Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Clorox reports an 8% revenue decline for Q1 CY2025, impacted by shifting consumer sentiment and competition. Despite challenges, Clorox maintains market share and focuses on operational efficiency.

This article investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets. The documented relation is significantly stronger for the periods during which (i) S&P 500 constituent firms announce their earnings; (ii) cash flow and discount rate news are large in magnitude; and (iii) consumer sentiment index takes extreme values. The intertemporal relation remains strongly negative after controlling for conditional volatility, variance risk premium, and macroeconomic variables. Moreover, a trading strategy based on the intertemporal relation with volatility spreads has higher portfolio returns compared to a passive strategy of investing in the S&P 500 index, after transaction costs are taken into account.

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.

Short/Long Term Stocks: SPB Stock Forecast

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.

Consumer Confidence: A Tale of Resilience or False Hope?

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 Switzerland increased to -32 points in June from -37 points in May of 2025. This dataset provides - Switzerland Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Innovations to consumer confidence convey incremental information about economic activity far into the future. Does this reflect a causal effect of animal spirits on economic activity, or news about exogenous future productivity received by consumers? Using indirect inference, we study the impulse responses to confidence innovations in conjunction with an appropriately augmented New Keynesian model. While news, animal spirits, and pure noise all contribute to confidence innovations, the relationship between confidence and subsequent activity is almost entirely reflective of the news component. Confidence innovations are well characterized as noisy measures of changes in expected productivity growth over a relatively long horizon. (JEL D12, D83, D84, E12)

Usecase/Applications possible with the data:

Customer feedback analysis: Analyzing customer feedback can be helpful for businesses to keep customers happy, stay loyal to the brand, and identify any areas to improve.

Social media monitoring: With sentiment analysis, companies can monitor what's being said about them on social media and use that to figure out how people feel about their products and services and track any new trends.

Market research: Sentiment analysis can be used to analyze market trends and consumer preferences, which can help companies make informed business decisions and develop effective marketing strategies.

Financial analysis: You can use sentiment analysis to determine what people say about the stock market through news and social media, which can help you make investing decisions.

For e-commerce (amazon/Bestbuy/home depot and much more) following data fields can be included: Title Price Vendor Name Ratings Reviews Brand ASIN URL Sentiment analysis for each review And other fields, as per request

Our study analyzed the impact of African swine fever (ASF) news on the Korean meat market using sentiment analysis. We applied a neural network language model (NNLM) to generate a sentiment index indicating whether the news had a positive or negative impact on consumer expectations. We analyzed 24,143 news articles to estimate the impulse responses of meat price variables to sentiment shocks. Our study contributes significantly to agricultural economics as it applies NNLM to generate a sentiment index. The empirical results indicated that ASF news sentiment has a substantial impact on meat prices in Korea, and there is evidence of substitution effects among different types of meat. ASF news has a positive impact on the price of pork, negative effects on beef and chicken prices, and a greater impact on the price of chicken than beef. The findings imply that the effect of ASF news on demand outweighs its impact on supply in the pork market, whereas the effect on supply surpasses the effect on demand in the beef and chicken market. We believe our methods and results will inspire discussions among applied economists studying consumer behavior in this specific market and could encourage the application of big data analysis to the agricultural economy.