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NASDAQ (National Association of Securities Dealers Automated Quotation) is the world's second largest automated and electronic stock exchange and securities market in the United States, the first being the New York Stock Exchange, with more than 8,000 companies and corporations. It has more trading volume per hour than any other stock exchange in the world. More than 7,000 small and mid-cap stocks are traded on the NASDAQ. It is characterized by comprising high-tech companies in electronics, computers, telecommunications, biotechnology, and many others.
This dataset was created as a result of an automatic extraction of open & public data available in nasdaq.com, using web scraping techniques. The only purpose of creating it was for academic reasons
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The main stock market index of United States, the US500, rose to 6211 points on July 1, 2025, gaining 0.10% from the previous session. Over the past month, the index has climbed 4.64% and is up 12.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.
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United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.
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Files:- stock_exchanges_data.csv:This file provides data on key financial indicators for 82 global stock exchanges, including Market Capitalization, Capitalization-to-GDP Ratio, Value Traded, Value Traded to GDP Ratio, Share Turnover Velocity, Capitalization per Listed Company, and Number of Trades. The data reflects the year 2023 and serves as the foundation for clustering and classification analysis within the study, focusing on identifying development patterns and key factors influencing stock exchange stability and competitiveness.- research_code.ipynb:This Jupyter Notebook contains the complete Python code used for the analysis conducted in the study. It includes data preparation, clustering, classification, Shapley values calculation, and all other analytical steps described in the paper. The notebook is fully reproducible based on the provided dataset.Raw data (csv files). Source: The World Federation of Exchanges (WFE) and International Monetary Fund (IMF)
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This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.
This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.
Ticker
: The stock symbol (e.g., AAPL, TSLA) Quantity
: The number of shares in the portfolio Sector
: The sector the stock belongs to (e.g., Technology, Healthcare) Close
: The closing price of the stock Weight
: The weight of the stock in the portfolio (as a percentage of total portfolio)This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.
Date
: The date of the data point Ticker
: The stock symbol Open
: The opening price of the stock on that day High
: The highest price reached on that day Low
: The lowest price reached on that day Close
: The closing price of the stock Adjusted
: The adjusted closing price after stock splits and dividends Returns
: Daily percentage return based on close prices Volume
: The volume of shares traded that dayThis file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.
Date
: The date of the data point Ticker
: The stock symbol (for NASDAQ index, this will be "IXIC") Open
: The opening price of the index High
: The highest value reached on that day Low
: The lowest value reached on that day Close
: The closing value of the index Adjusted
: The adjusted closing value after any corporate actions Returns
: Daily percentage return based on close values Volume
: The volume of shares tradedThis file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.
Date
: The date of the data point Ticker
: The stock symbol (for S&P 500 index, this will be "SPX") Open
: The opening price of the index High
: The highest value reached on that day Low
: The lowest value reached on that day Close
: The closing value of the index Adjusted
: The adjusted closing value after any corporate actions Returns
: Daily percentage return based on close values Volume
: The volume of shares tradedThis file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.
Date
: The date of the data point Ticker
: The stock symbol (for Dow Jones index, this will be "DJI") Open
: The opening price of the index High
: The highest value reached on that day Low
: The lowest value reached on that day Close
: The closing value of the index Adjusted
: The adjusted closing value after any corporate actions Returns
: Daily percentage return based on close values Volume
: The volume of shares tradedThis data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.
This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.
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Egypt EG: Stocks Traded: Total Value data was reported at 14.429 USD bn in 2017. This records an increase from the previous number of 10.080 USD bn for 2016. Egypt EG: Stocks Traded: Total Value data is updated yearly, averaging 21.767 USD bn from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 95.827 USD bn in 2008 and a record low of 10.080 USD bn in 2016. Egypt EG: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.
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Japan's main stock market index, the JP225, rose to 40151 points on June 27, 2025, gaining 1.43% from the previous session. Over the past month, the index has climbed 6.44% and is up 1.43% compared to the same time last year, 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 June of 2025.
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Data retrieved from world-exchanges.org Statistics portal. It comprises of 19 of the largest market capitalization stock exchanges from around the world, with a minimum 1 trillion USD valuation in 2023. The cleaned version is a wide dataframe with exchange name, region, and its valuation across the years.
My work can be seen at:
Code: https://github.com/DanielDataGit/daniel.R/blob/main/Work.R/LIS4370Rprog/4317FinalProjectCode.R
Blog: https://danieltlis4317.blogspot.com/2024/11/final-project.html
MIT Licensehttps://opensource.org/licenses/MIT
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Global Top Index: Exploring Trends in Stock Markets
About the Dataset
The Global Top Index dataset offers a detailed view of daily trading activities from several of the world's leading stock market indices. This dataset is ideal for conducting comprehensive analyses to uncover insights and predictive trends in the international stock markets.
Dataset Contents
The dataset encompasses the following key data points for each trading session across multiple dates… See the full description on the dataset page: https://huggingface.co/datasets/pettah/global-top-Index-exploring-trends-in-stock-Market.
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China's main stock market index, the SHANGHAI, rose to 3448 points on July 1, 2025, gaining 0.11% from the previous session. Over the past month, the index has climbed 2.57% and is up 15.06% 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.
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The Yahoo Stocks Dataset is an invaluable resource for analysts, traders, and developers looking to enhance their financial data models or trading strategies. Sourced from Yahoo Finance, this dataset includes historical stock prices, market trends, and financial indicators. With its accurate and comprehensive data, it empowers users to analyze patterns, forecast trends, and build robust machine learning models.
Whether you're a seasoned stock market analyst or a beginner in financial data science, this dataset is tailored to meet diverse needs. It features details like stock prices, trading volume, and market capitalization, enabling a deep dive into investment opportunities and market dynamics.
For machine learning and AI enthusiasts, the Yahoo Stocks Dataset is a goldmine. It’s perfect for developing predictive models, such as stock price forecasting and sentiment analysis. The dataset's structured format ensures seamless integration into Python, R, and other analytics platforms, making data visualization and reporting effortless.
Additionally, this dataset supports long-term trend analysis, helping investors make informed decisions. It’s also an essential resource for those conducting research in algorithmic trading and portfolio management.
Key benefits include:
Download the Yahoo Stocks Dataset today and harness the power of financial data for your projects. Whether for AI, financial reporting, or trend analysis, this dataset equips you with the tools to succeed in the dynamic world of stock markets.
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United States US: Market Capitalization: Listed Domestic Companies data was reported at 32,120.703 USD bn in 2017. This records an increase from the previous number of 27,352.201 USD bn for 2016. United States US: Market Capitalization: Listed Domestic Companies data is updated yearly, averaging 11,322.354 USD bn from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 32,120.703 USD bn in 2017 and a record low of 1,263.561 USD bn in 1981. United States US: Market Capitalization: Listed Domestic Companies data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSO20g5cBn_b3UvD4HrPSKMrujGXq8LfT2NQP3LC3F3k8ufSV6TP97l7Har-625Bju08bc&usqp=CAU" alt="File:Yahoo Finance Logo 2013.svg - Wikipedia">
Yahoo! Finance is a media property that is part of the Yahoo! network. It provides financial news, data and commentary including stock quotes, press releases, financial reports, and original content. It also offers some online tools for personal finance management. In addition to posting partner content from other web sites, it posts original stories by its team of staff journalists. It is ranked 20th by Similar Web on the list of largest news and media websites.
###
python
1.Content:
2.Symbol:
3.Name:
4.Price:
5.Volume:
6.Market cap:
7.P/E ratio:
The data is sourced from Yahoo Finance and is updated daily, providing users with the most up-to-date financial information for each company listed.
The dataset is suitable for anyone interested in analyzing or predicting stock market trends and is particularly useful for financial analysts, investors, and traders.
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Vietnam VN: Stocks Traded: Total Value data was reported at 38.060 USD bn in 2017. This records an increase from the previous number of 22.272 USD bn for 2016. Vietnam VN: Stocks Traded: Total Value data is updated yearly, averaging 19.144 USD bn from Dec 2008 (Median) to 2017, with 9 observations. The data reached an all-time high of 38.060 USD bn in 2017 and a record low of 7.057 USD bn in 2008. Vietnam VN: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.
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This dataset is about book subjects. It has 1 row and is filtered where the books is Global stock markets : a strategic guide. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.
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License information was derived automatically
This dataset is about stocks. It has 4 rows and is filtered where the company is Global. It features 8 columns including stock name, company, exchange, and exchange symbol.
By Jon Loyens [source]
This powerful dataset brings together publically-available information from leading stock markets with extensive details about corporate board members. For each company, discover not only their board composition and background, but also current market dynamics, trends and rule changes affecting them. Whether you're a teacher looking to add more detail to a class presentation or an investor seeking a competitive edge in the market - this dataset provides comprehensive insights into the world of stocks and those that play an influential role on its direction. Unprecedented access awaits as you explore hypothetical investments and strategies or actual risks associated with established entities today
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
Using this dataset, you can gain a better understanding of the relationship between corporate board members and stock market performance. You can analyze the data to determine the average performance of board members at different companies and compare it to the overall performance of other stocks. In addition, you can look into correlations between individual stocks, various industries, and different groups of companies with similar board membership profiles. This dataset provides an overview of all major stocks across multiple industries with detailed insights on each stock's current and past market performance as well as corporate boards
- Analyzing the performance of individual board members in relation to their company’s stock market performance.
- Determining if certain board members are better at making decisions that benefit the company’s stock market position across all companies they have a stake in.
- Identifying correlations between trends in different companies' stocks and external factors such as the influence of particular board members or other events associated with that company's sectors or markets
If you use this dataset in your research, please credit the original authors. Data Source
See the dataset description for more information.
File: boardmembers.csv | Column name | Description | |:--------------------|:-----------------------------------| | BoardMemberName | Name of the board member. (String) | | CompanyName | Name of the company. (String) | | Source | Source of the data. (String) |
If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jon Loyens.
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France's main stock market index, the FR40, rose to 7694 points on June 30, 2025, gaining 0.03% from the previous session. Over the past month, the index has declined 0.56%, though it remains 1.76% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on June of 2025.
Techsalerator's Corporate Actions Dataset in South Korea offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 2445 companies traded on the Korea Stock Exchange (XKRX).
Top 5 used data fields in the Corporate Actions Dataset for South Korea:
Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.
Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.
Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.
Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.
Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.
Top 5 corporate actions in South Korea:
Mergers and Acquisitions (M&A): South Korea's business landscape has seen various corporate actions related to mergers, acquisitions, and corporate restructuring, contributing to industry consolidation and market dynamics.
Technological Innovation: Corporate actions involving investments in technology, research and development, and innovation have been prominent in South Korea's efforts to maintain its position as a global technology leader.
Global Expansion: South Korean companies have undertaken corporate actions to expand their global footprint, including entering new markets, forming strategic partnerships, and exploring joint ventures.
Renewable Energy Initiatives: Corporate actions related to the renewable energy sector, including investments in solar, wind, and other green technologies, align with South Korea's push for sustainable development.
Financial Sector Developments: Corporate actions involving financial institutions, fintech advancements, and regulatory changes contribute to the modernization and competitiveness of South Korea's financial industry.
Top 5 financial instruments with corporate action Data in South Korea
Seoul Stock Exchange (SSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Seoul Stock Exchange. This index would provide insights into the performance of the South Korean stock market.
Seoul Stock Exchange (SSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Seoul Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in South Korea.
KorMart: A South Korea-based online marketplace with operations in multiple regions. KorMart focuses on connecting buyers and sellers and contributing to the growth of e-commerce in South Korea.
FinanceKorea: A financial services provider in South Korea with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.
TechInnovate Korea: A company dedicated to advancing technological innovation in South Korea, focusing on research and development, and fostering a culture of innovation to support the country's technology sector.
If you're interested in accessing Techsalerator's End-of-Day Pricing Data for South Korea, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.
Data fields included:
Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price
Q&A:
How much does the Corporate Actions Dataset cost in South Korea?
The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.
How complete is the Corporate Actions Dataset coverage in South Korea?
Techsalerator provides comprehensive coverage of Corporate Act...
TagX is your trusted partner for stock market and financial data solutions. We specialize in delivering real-time and end-of-day data feeds that power software, trading algorithms, and risk management systems globally. Whether you're a financial institution, hedge fund, or individual investor, our reliable datasets provide essential insights into market trends, historical pricing, and key financial metrics.
TagX is committed to precision and reliability in stock market data. Our comprehensive datasets include critical information such as date, open/close/high/low prices, trading volume, EPS, P/E ratio, dividend yield, and more. Tailor your dataset to match your specific requirements, choosing from a wide range of parameters and coverage options across primary listings on NASDAQ, AMEX, NYSE, and ARCA exchanges.
Key Features of TagX Stock Market Data:
Custom Dataset Requests: Customize your data feed to focus on specific metrics and parameters crucial to your trading strategy.
Extensive Coverage: Access data from reputable exchanges and market participants, ensuring accuracy and completeness in your analyses.
Flexible Pricing Models: Choose pricing structures based on your selected parameters, offering cost-effective solutions tailored to your needs.
Why Choose TagX? Partner with TagX for precise, dependable, and customizable stock market data solutions. Whether you require real-time updates or end-of-day valuations, our datasets are designed to support informed decision-making and enhance your competitive edge in the financial markets. Trust TagX to deliver the data integrity and accuracy essential for maximizing your trading potential.
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NASDAQ (National Association of Securities Dealers Automated Quotation) is the world's second largest automated and electronic stock exchange and securities market in the United States, the first being the New York Stock Exchange, with more than 8,000 companies and corporations. It has more trading volume per hour than any other stock exchange in the world. More than 7,000 small and mid-cap stocks are traded on the NASDAQ. It is characterized by comprising high-tech companies in electronics, computers, telecommunications, biotechnology, and many others.
This dataset was created as a result of an automatic extraction of open & public data available in nasdaq.com, using web scraping techniques. The only purpose of creating it was for academic reasons