https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
By [source]
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!
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
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”
- 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
If you use this dataset in your research, please credit the original authors. Data Source
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.
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) ...
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Corporate Profits in the United States increased to 3266.20 USD Billion in the second quarter of 2025 from 3203.60 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Key information about China Market Capitalization
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
License information was derived automatically
The research data in this article comes from the data of Chinese A-share listed companies from 2000 to 2023; The annual reports of relevant companies are obtained from the official websites of the Shenzhen and Shanghai Stock Exchanges; The relevant data of listed companies comes from the CSMAR database of Guotai An. At the same time, this article conducts a 1% truncation process on non ratio continuous variables to reduce the impact of outliers. (1) Due to the lack of mandatory disclosure of carbon emission data by the Chinese government, there is currently a lack of micro level data on corporate carbon emissions. This study adopted the method of Chapple et al. (2013) to indirectly measure the carbon dioxide emissions of enterprises. Due to the lack of 23 years of carbon emission data, this study borrowed the ARIMA-BP prediction method from Hu Jianbo (2013) and Zhao SL et al. (2024) to fill in the predictions. (2) The degree of digital transformation of listed companies (Digital) The measurement methods for digital transformation of companies are relatively mature, and the measurement method adopts text analysis. This article first constructs numbers Keyword table for transformation; Then use Python software to match the vocabulary with the text of the annual report of the listed company, and use Jieba's method The module can calculate the frequency of relevant keywords appearing in the annual report documents of listed companies; Finally, add 1 to the frequency of the word and perform logarithmic processing Obtain indicators for enterprise digital transformation. Please refer to Wu Fei's (2021) approach (Managing the World) for specific details. (3) Control variables. This study includes enterprise level indicators as control variables: property rights nature of enterprises (SOE), with state-owned enterprises set to 0 and private enterprises set to 0 1) For operating enterprises, the board size (logarithm of the number of board members), the logarithm of the age of the enterprise (age), and the assets and liabilities Rate (lev), return on equity (roe), operating cash flow (CF), sales growth rate (growth), net profit growth rate (gprofit) Proportion of tangible assets (tangibi), proportion of independent directors (indep), proportion of the largest shareholder's shareholding (top 1) The dual role of chairman and general manager, and the nature of property rights (SOE). This study was supported by the Key Support Project for College Students' Innovation and Entrepreneurship in Hunan Province - Research on the Factors and Mechanisms of Digital Transformation of Construction Enterprises in the Digital Economy (S202411532001)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Corn rose to 399.02 USd/BU on September 1, 2025, up 0.26% from the previous day. Over the past month, Corn's price has risen 3.11%, but it is still 0.49% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on September of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.
https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval
View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.
With a market capitalization of 3.12 trillion U.S. dollars as of May 2024, Microsoft was the world’s largest company that year. Rounding out the top five were some of the world’s most recognizable brands: Apple, NVIDIA, Google’s parent company Alphabet, and Amazon. Saudi Aramco led the ranking of the world's most profitable companies in 2023, with a pre-tax income of nearly 250 billion U.S. dollars. How are market value and market capitalization determined? Market value and market capitalization are two terms frequently used – and confused - when discussing the profitability and viability of companies. Strictly speaking, market capitalization (or market cap) is the worth of a company based on the total value of all their shares; an important metric when determining the comparative value of companies for trading opportunities. Accordingly, many stock exchanges such as the New York or London Stock Exchange release market capitalization data on their listed companies. On the other hand, market value technically refers to what a company is worth in a much broader context. It is determined by multiple factors, including profitability, corporate debt, and the market environment as a whole. In this sense it aims to estimate the overall value of a company, with share price only being one element. Market value is therefore useful for determining whether a company’s shares are over- or undervalued, and in arriving at a price if the company is to be sold. Such valuations are generally made on a case-by-case basis though, and not regularly reported. For this reason, market capitalization is often reported as market value. What are the top companies in the world? The answer to this question depends on the metric used. Although the largest company by market capitalization, Microsoft's global revenue did not manage to crack the top 20 companies. Rather, American multinational retailer Walmart was ranked as the largest company in the world by revenue. Walmart also had the highest number of employees in the world.
The average market risk premium in the United States decreased slightly to *** percent in 2023. This suggests that investors demand a slightly lower return for investments in that country, in exchange for the risk they are exposed to. This premium has hovered between *** and *** percent since 2011. What causes country-specific risk? Risk to investments come from two main sources. First, inflation causes an asset’s price to decrease in real terms. A 100 U.S. dollar investment with three percent inflation is only worth ** U.S. dollars after one year. Investors are also interested in risks of project failure or non-performing loans. The unique U.S. context Analysts have historically considered the United States Treasury to be risk-free. This view has been shifting, but many advisors continue to use treasury yield rates as a risk-free rate. Given the fact that U.S. government securities are available at a variety of terms, this gives investment managers a range of tools for predicting future market developments.
According to estimates, Amazon claimed the top spot among online retailers in the United States in 2023, capturing 37.6 percent of the market. Second place was occupied by the e-commerce site of the retail chain Walmart, with a 6.4 percent market share, followed in third place by Apple, with 3.6 percent.
Amazon’s continued success
Amazon has long dominated the e-commerce market as the world’s favorite online marketplace. In 2022, company hit over half a trillion U.S. dollars in net sales. The United States is by far Amazon’s most profitable market, as the U.S. branch generated over 356 billion U.S. dollars in sales in 2022. Germany ranked second, with 33 billion dollars, followed closely by the United Kingdom with 30 billion dollars.
Online shopping on the rise
Online shopping has grown significantly over the past decade, with more people turning to the internet for their shopping needs. The proof is in the numbers: the U.S. e-commerce industry was worth almost a trillion dollars in 2023. By 2027, forecasts show that the online market will grow to more than 50 percent. U.S. online shoppers purchase fashion and food and beverages the most via the internet.
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https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
By [source]
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!
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
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”
- 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
If you use this dataset in your research, please credit the original authors. Data Source
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.
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) ...