Updated investor sentiment index dataset up to December 2014 (including both Baker and Wurgler's sentiment index, and Huang, Jiang, Tu and Zhou (2015 RFS)'s investor sentiment index)

The Enhanced Investor Sentiment Index (STV) is an improved measure of investor sentiment, allowing contributions of each component of the index to vary over time instead of being fixed, as in the Baker and Wurgler (2006) investor sentiment index. STV has a better forecasting power and contains unique information about future market returns.

In the second quarter of 2025, the real estate index in Poland amounted to ***** points, which was an improvement of **** points compared to the first quarter of 2025.

Browse LSEG's Consensus Bullish Sentiment Index and find unique sentiment index indicators for the commodities market.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Business Investment And Sentiment (EMVMACROBUS) from Jan 1985 to Aug 2025 about volatility, uncertainty, equity, investment, business, and USA.

The Consumer Sentiment Index in the United States stood at 64.7 in January 2025, an increase from the previous month. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

Brain Sentiment Indicator [version Currencies, Cryptocurrencies and Commodities] monitors public financial news for 8 currencies, more than 10 cryptocurrencies and more than 60 commodities from about 2000 financial media sources in 33 languages.

The sentiment scoring technology is based on a combination of various natural language processing techniques.

The sentiment score assigned to each stock is a value ranging from -1 (most negative) to +1 (most positive) that is updated with a daily frequency. The sentiment score corresponds to the average of sentiment for each piece of news and it is available on two time scales; 7 days and 30 days.

1. Financial news are collected every few minutes from various financial media

2. Brain engine assigns a specific category to each piece of news (e.g. “patent win” or “contract lose”) using semantic rules. Each category has a predefined value of sentiment.

3. If the categorization fails a bag of words approach is used based on dictionaries customized for Financial news. The approach includes a strategy for negation handling.

4. Repetition of similar news is kept into account in the sentiment aggregation.

The sentiment data for each piece of news is averaged on two time scales, considering the piece of news of last 7 days and of last 30 days. The data are exported daily and are available by 6.00 AM UTC on a dedicated S3 bucket..

Description

This dataset, titled "Cryptocurrency Market Sentiment & Prediction," is a synthetic collection of real-time crypto market data designed for advanced analysis and predictive modeling. It captures a comprehensive range of features including price movements, social sentiment, news impact, and trading patterns for 10 major cryptocurrencies. Tailored for data scientists and analysts, this dataset is ideal for exploring market volatility, sentiment analysis, and price prediction, particularly in the context of significant events like the Bitcoin halving in 2024 and increasing institutional adoption.

Key Features Overview: - Price Movements: Tracks current prices and 24-hour price change percentages to reflect market dynamics. - Social Sentiment: Measures sentiment scores from social media platforms, ranging from -1 (negative) to 1 (positive), to gauge public perception. - News Sentiment and Impact: Evaluates sentiment from news sources and quantifies their potential impact on market behavior. - Trading Patterns: Includes data on 24-hour trading volumes and market capitalization, crucial for understanding market activity. - Technical Indicators: Features metrics like the Relative Strength Index (RSI), volatility index, and fear/greed index for in-depth technical analysis. - Prediction Confidence: Provides a confidence score for predictive models, aiding in assessing forecast reliability.

Purpose and Applications: - Perfect for machine learning tasks such as price prediction, sentiment-price correlation studies, and volatility classification. - Supports time series analysis for forecasting price movements and identifying volatility clusters. - Valuable for research into the influence of social media and news on cryptocurrency markets, especially during high-impact events.

Dataset Scope: - Covers a simulated 30-day period, offering a snapshot of market behavior under varying conditions. - Focuses on major cryptocurrencies including Bitcoin, Ethereum, Cardano, Solana, and others, ensuring relevance to current market trends.

Dataset Structure Table:

Dataset Statistics Table:

This dataset is a powerful resource for machine learning projects, sentiment analysis, and crypto market research, providing a robust foundation for AI/ML model development and testing.

Short interest is a market-sentiment indicator that tells whether investors think a stock's price is likely to fall. It can also be compared over time to examine changes in investor sentiment.

Short interest regulation and reporting requirements vary by country. Countries with Short Interest Data by Position Holder

-Austria, Belgium, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Netherlands, Poland, Portugal, Spain, Sweden, UK, Japan Data for these countries is reported to local regulators in compliance with ESMA short selling regulations and began for most of these markets on 1 November 2012. The exceptions to this are Spain, which has data going back to 10 June 2010 and Greece, where the history begins on30 May 2013.

Countries with Short Interest Data by Traded Volume/Position

-Canada, China, Chile, Hong Kong, Israel, Malaysia, Mexico, New Zealand, Norway, Peru, Singapore, South Korea, Taiwan, Thailand, Turkey, United States, Brazil, Australia.

Countries Which Permit Short Selling but Have no Activity

-following countries permit short selling, but there is currently no activity. EDI monitors these markets and will provide updates if / when there is activity:

Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, India, Latvia, Lithuania, Luxembourg, Malta, Philippines, Romania, Saudi Arabia, and Slovakia.

Economic Optimism Index In the Euro Area decreased to 95.20 points in August from 95.70 points in July of 2025. This dataset provides - Euro Area Economic Sentiment Indicator- actual values, historical data, forecast, chart, statistics, economic calendar and news.

View weekly updates and historical trends for US Investor Sentiment, % Bearish. from United States. Source: The American Association of Individual Investo…

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

United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data was reported at 11.000 % in Oct 2018. This records an increase from the previous number of 10.000 % for Sep 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data is updated monthly, averaging 6.000 % from Jun 2002 (Median) to Oct 2018, with 196 observations. The data reached an all-time high of 13.000 % in Jan 2018 and a record low of 1.000 % in Nov 2011. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s United States – Table US.H029: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the percent change that this one thousand dollar investment will increase in value in the year ahead, so that it is worth more than one thousand dollars one year from now?

Consumer Confidence in the United States decreased to 58.20 points in August from 61.70 points in July 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.

Economic Optimism Index in European Union decreased to 94.90 points in August from 95.20 points in July of 2025. This dataset provides - European Union Economic Sentiment Indicator- 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.

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

EDI tracks and collects index notifications from a wide range of index providers and covers many financial market indices, including stock and bond indices as well as economic indicators. Components for over 6000 Indices worldwide

Indices Data. The components are updated daily. Historical components lists are available based on legal advice. Index components weighting are not offered.

Using the EDI SFTP Server, you will receive the daily index composition of the indices that you subscribe to. The files are provided as txt.csv or xls format. EDI provides a free coverage check and samples of the index components that are of interest to you.

United States CSI: Savings: Stock Market Increase Probability: Next Yr: 51-74% data was reported at 13.000 % in May 2018. This records a decrease from the previous number of 16.000 % for Apr 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 51-74% data is updated monthly, averaging 15.000 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 24.000 % in Apr 2015 and a record low of 6.000 % in Mar 2009. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 51-74% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the percent change that this one thousand dollar investment will increase in value in the year ahead, so that it is worth more than one thousand dollars one year from now?

data of study

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.

MSCI World: Reflecting Global Economic Trends or Inflated Valuations?

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