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

This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

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.

What is the stock market doing today?

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 How investors impound qualitative information released by the media into prices, especially in a less efficient market such as Brazil, helps understand the types of news most sensitive to investors. This study investigates the relationship between the content of the daily editions of specialized financial media in Brazil, captured by a metric of textual tone, and returns and volatility of market indexes. Our database contains 1,237 daily editions of the newspaper “Valor Econômico,” between 01/02/2012 and 12/30/2016. The results indicate that the market put more weight on the words “uncertainty” and “negative” in the news. “Uncertainty” has negative relation to current market-returns and weak evidence that news with “negative” terms have positive associations with current market-volatility. The evidences obtained point to the existence of informative content in the news pub lished by specialized media in Brazil, especially with the words “negative” and “uncertainty.”

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.

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

This dataset is designed to advance labelled financial sentiment analysis research. It combines two notable datasets, FiQA and Financial PhraseBank, into a single, easy-to-use CSV file. The primary purpose is to provide financial sentences accompanied by their corresponding sentiment labels, which can be positive, negative, or neutral. This resource is valuable for understanding market and corporate sentiment expressed in textual data.

Columns

The dataset is structured with at least two key columns: * Sentence: This column contains the textual financial statement or phrase. * Sentiment Label: This column provides the associated sentiment of the sentence, categorised as 'positive', 'negative', or 'neutral'.

Distribution

The dataset is provided in a CSV file format. It organises financial sentences with their assigned sentiment labels. Specific details regarding the exact number of rows or records are not available in the provided information.

Usage

This dataset is ideal for various applications and use cases, including: * Developing and testing Natural Language Processing (NLP) models for sentiment detection in financial texts. * Conducting data science and analytics projects focused on market dynamics and corporate communications. * Building tools for business intelligence to gauge sentiment from financial news and reports. * Academic research into the nuances of economic language and its emotional tone.

Coverage

The dataset's regional scope is global. The financial sentences included refer to various companies and market events, with examples from periods such as 2008 and 2010. While a precise time range for all data points is not specified, the content is relevant to corporate financial and market sentiment over several years. There are no specific notes on demographic scope; the focus is on business and financial entities.

License

CCO

Who Can Use It

This dataset is particularly suited for: * Researchers keen on exploring financial sentiment analysis techniques and models. * Data Scientists working on machine learning applications for textual data in the finance domain. * Financial Analysts looking to integrate sentiment indicators into their market assessments. * Developers creating applications that require understanding the emotional tone of financial statements.

Dataset Name Suggestions

• Financial Sentence Sentiment Corpus
• Global Financial Sentiment Labeled Data
• Market Sentiment Analysis Dataset
• Corporate Financial Text Sentiment

Attributes

Original Data Source:Financial Sentiment Analysis

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.

Germany Stock Market Expectation: Japan data was reported at 37.500 % in Mar 2021. This records a decrease from the previous number of 37.800 % for Feb 2021. Germany Stock Market Expectation: Japan data is updated monthly, averaging 34.600 % from Dec 1991 (Median) to Mar 2021, with 352 observations. The data reached an all-time high of 74.600 % in Dec 1999 and a record low of -8.200 % in Jun 2020. Germany Stock Market Expectation: Japan data remains active status in CEIC and is reported by Leibniz Centre for European Economic Research. The data is categorized under Global Database’s Germany – Table DE.S001: Indicator of Economic Sentiment: ZEW.

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.

Japan's main stock market index, the JP225, rose to 40065 points on July 17, 2025, gaining 1.01% from the previous session. Over the past month, the index has climbed 3.03%, though it remains 0.15% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on July of 2025.

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.

What is forex trading?

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 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.

China's main stock market index, the SHANGHAI, rose to 3534 points on July 18, 2025, gaining 0.50% from the previous session. Over the past month, the index has climbed 5.13% and is up 18.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Tweet Sentiment's Impact on Stock Returns

862,231 Labeled Instances

By [source]

About this dataset

This dataset contains 862,231 labeled tweets and associated stock returns, providing a comprehensive look into the impact of social media on company-level stock market performance. For each tweet, researchers have extracted data such as the date of the tweet and its associated stock symbol, along with metrics such as last price and various returns (1-day return, 2-day return, 3-day return, 7-day return). Also recorded are volatility scores for both 10 day intervals and 30 day intervals. Finally, sentiment scores from both Long Short - Term Memory (LSTM) and TextBlob models have been included to quantify the overall tone in which these messages were delivered. With this dataset you will be able to explore how tweets can affect a company's share prices both short term and long term by leveraging all of these data points for analysis!

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How to use the dataset

In order to use this dataset, users can utilize descriptive statistics such as histograms or regression techniques to establish relationships between tweet content & sentiment with corresponding stock return data points such as 1-day & 7-day returns measurements.

The primary fields used for analysis include Tweet Text (TWEET), Stock symbol (STOCK), Date (DATE), Closing Price at the time of Tweet (LAST_PRICE) a range of Volatility measures 10 day Volatility(VOLATILITY_10D)and 30 day Volatility(VOLATILITY_30D ) for each Stock which capture changes in market fluctuation during different periods around when Twitter reactions occur. Additionally Sentiment Polarity analysis undertaken via two Machine learning algorithms LSTM Polarity(LSTM_POLARITY)and Textblob polarity provide insight into whether people are expressing positive or negative sentiments about each company at given times which again could influence thereby potentially influence Stock Prices over shorter term periods like 1-Day Returns(1_DAY_RETURN),2-Day Returns(2_DAY_RETURN)or longer term horizon like 7 Day Returns*7DAY RETURNS*.Finally MENTION field indicates if names/acronyms associated with Companies were specifically mentioned in each Tweet or not which gives extra insight into whether company specific contexts were present within individual Tweets aka “Company Relevancy”

Research Ideas

• Analyzing the degree to which tweets can influence stock prices. By analyzing relationships between variables such as tweet sentiment and stock returns, correlations can be identified that could be used to inform investment decisions.
• Exploring natural language processing (NLP) models for predicting future market trends based on textual data such as tweets. Through testing and evaluating different text-based models using this dataset, better predictive models may emerge that can give investors advance warning of upcoming market shifts due to news or other events.
• Investigating the impact of different types of tweets (positive/negative, factual/opinionated) on stock prices over specific time frames. By studying correlations between the sentiment or nature of a tweet and its effect on stocks, insights may be gained into what sort of news or events have a greater impact on markets in general

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: reduced_dataset-release.csv | Column name | Description | |:----------------------|:-------------------------------------------------------------------------------------------------------| | TWEET | Text of the tweet. (String) | | STOCK | Company's stock mentioned in the tweet. (String) | | DATE | Date the tweet was posted. (Date) | | LAST_PRICE | Company's last price at the time of tweeting. (Float) ...

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.

Nifty 50: Climb or Crash?

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

Economic Optimism Index In the Euro Area decreased to 94 points in June from 94.80 points in May of 2025. This dataset provides - Euro Area Economic Sentiment Indicator- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Recent research has found that monetary policy works in part by influencing the risk premiums on both traded financial-market securities and intermediated loans. Research has also shown that when risk premiums are compressed, there is an increased likelihood of a reversal that damages the credit-supply mechanism and the real economy. Together these effects create an intertemporal tradeoff for monetary policy, as stimulating the economy today can sow the seeds of a future downturn that might be difficult to offset. We introduce a simple model of this tradeoff and draw out its implications for the conduct of monetary policy.

Sentiment Analysis Software Market Outlook

The global sentiment analysis software market size was valued at approximately $3.5 billion in 2023 and is projected to reach around $8.7 billion by 2032, growing at a CAGR of 10.8% during the forecast period. The burgeoning growth of this market is largely attributed to the increasing need for actionable insights into consumer behavior and preferences, which is driving enterprises to adopt sentiment analysis tools. The relentless expansion of digital business operations and the integration of advanced analytics to understand customer sentiment further augment market growth. The demand for real-time sentiment analysis is becoming a crucial component for businesses aiming to enhance customer experience and tailor their products and services accordingly.

One of the primary growth factors for the sentiment analysis software market is the rapid adoption of social media platforms and the proliferation of digital content. With consumers increasingly expressing their opinions and preferences online, businesses are compelled to utilize sentiment analysis tools to sift through massive volumes of data and derive meaningful insights. This trend is further fueled by the need for businesses to maintain a competitive edge by understanding market trends and consumer sentiment. Additionally, the integration of machine learning and natural language processing technologies into sentiment analysis software is enhancing its accuracy and efficiency, thereby boosting its adoption across various industries.

Moreover, the market is experiencing significant growth due to the rising demand for customer experience management solutions. With customer satisfaction becoming a pivotal focus for businesses, sentiment analysis software is being leveraged to monitor and analyze customer feedback in real-time. This allows companies to make informed decisions and implement strategies that improve customer engagement and loyalty. The ability to anticipate customer needs and preferences through sentiment analysis is facilitating improved service delivery and product innovation, further driving the market's expansion.

Furthermore, the increasing adoption of cloud-based deployment models is also contributing to the market's growth. Cloud-based sentiment analysis solutions offer scalability, flexibility, and cost-effectiveness, making them ideal for businesses of all sizes. The ease of integration with existing systems and the ability to access insights remotely are encouraging organizations to transition from traditional on-premises solutions to cloud-based platforms. This shift is particularly beneficial for small and medium enterprises (SMEs) that seek to harness the power of sentiment analysis without incurring significant infrastructure costs.

Regionally, North America continues to dominate the sentiment analysis software market, driven by the presence of major technology companies and high adoption rates of advanced analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, propelled by increasing digitalization and the expanding e-commerce sector. Emerging economies in this region are embracing sentiment analysis tools to better understand consumer preferences and enhance competitiveness in the global market. Europe and Latin America are also witnessing significant growth, supported by technological advancements and a growing focus on improving customer satisfaction.

Component Analysis

The sentiment analysis software market is segmented into software and services, each playing a critical role in the adoption and implementation of sentiment analysis solutions. The software segment dominates the market, driven by the increasing demand for standalone and integrated solutions that offer capabilities such as text analytics, predictive analytics, and visualization tools. These software solutions are designed to cater to the diverse needs of businesses across various industries, providing them with the ability to analyze vast amounts of unstructured data efficiently.

Within the software segment, the integration of artificial intelligence (AI) and machine learning algorithms is a significant trend that is enhancing the functionality and accuracy of sentiment analysis tools. These technologies allow software solutions to learn from data, improve over time, and provide more precise insights into consumer sentiment. This is particularly beneficial for businesses that deal with large data volumes and require real-time analysis to make informed decisions. As a result, the demand for advanc

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.

Indonesia to Become a Global EV Battery Hub with $9 Billion Investment

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