This dataset offers a detailed collection of US-GAAP financial data extracted from the financial statements of exchange-listed U.S. companies, as submitted to the U.S. Securities and Exchange Commission (SEC) via the EDGAR database. Covering filings from January 2009 onwards, this dataset provides key financial figures reported by companies in accordance with U.S. Generally Accepted Accounting Principles (GAAP).

Dataset Features:

• Data Scope: The dataset is restricted to figures reported under US-GAAP standards, with the exception of EntityCommonStockSharesOutstanding and EntityPublicFloat.
• Currency and Units: The dataset exclusively includes figures reported in USD or shares, ensuring uniformity and comparability. It excludes ratios and non-financial metrics to maintain focus on financial data.
• Company Selection: The dataset is limited to companies with U.S. exchange tickers, providing a concentrated analysis of publicly traded firms within the United States.
• Submission Types: The dataset only incorporates data from 10-Q, 10-K, 10-Q/A, and 10-K/A filings, ensuring consistency in the type of financial reports analyzed.

Data Sources and Extraction:

This dataset primarily relies on the SEC's Financial Statement Data Sets and EDGAR APIs: - SEC Financial Statement Data Sets - EDGAR Application Programming Interfaces

In instances where specific figures were missing from these sources, data was directly extracted from the companies' financial statements to ensure completeness.

Please note that the dataset presents financial figures exactly as reported by the companies, which may occasionally include errors. A common issue involves incorrect reporting of scaling factors in the XBRL format. XBRL supports two tag attributes related to scaling: 'decimals' and 'scale.' The 'decimals' attribute indicates the number of significant decimal places but does not affect the actual value of the figure, while the 'scale' attribute adjusts the value by a specific factor.

However, there are several instances, numbering in the thousands, where companies have incorrectly used the 'decimals' attribute (e.g., 'decimals="-6"') under the mistaken assumption that it controls scaling. This is not correct, and as a result, some figures may be inaccurately scaled. This dataset does not attempt to detect or correct such errors; it aims to reflect the data precisely as reported by the companies. A future version of the dataset may be introduced to address and correct these issues.

The source code for data extraction is available here

Financial data service providers offer financial market data and related services, primarily real-time feeds, portfolio analytics, research, pricing and valuation data, to financial institutions, traders and investors. Companies aggregate data and content from stock exchange feeds, broker and dealer desks and regulatory filings to distribute financial news and business information to the investment community. Recent globalization of the world capital market has benefited the financial sector and increased trading speed. Businesses rely on real-time data more than ever to help them make informed decisions. When considering a data service provider, an easy-to-use interface that shows customized, relevant information is vital for clients. During times of economic uncertainty, this information becomes more crucial than ever. Clients want information as soon as and as frequently as possible, causing providers to prioritize efficiency and delivery. This was evident during the period as significant inflationary pressures resulted in substantial interest rate hikes throughout the period. However, as inflationary pressures eased, the Fed cut rates in the latter part of the period. Increased automation has enabled industry players to process large volumes of financial data more efficiently, thereby reducing analysis and reporting times. In addition, automation has reduced operational costs and reduced human data errors. These trends have resulted in growing revenue, which has risen at a CAGR of 3.0% to $23.4 billion over the past five years, including a 3.2% uptick in 2025 alone. Industry profit climbed and will account for 19.3% of revenue in the current year, as significant wage expenses lag. Corporate profit will continue to expand as inflationary concerns wane slowly. This will lead many companies to take on new clients as financial data helps them gain insight into operating their business amid ongoing trends and economic shakeups. With technology constantly advancing, service providers will continue investing in research and development to improve their products and services and best serve their clients. As technological advances continue, smaller players will be able to better compete with larger industry players. While this may lead to new companies joining the industry, larger providers will resume consolidation activity to expand their customer base. Overall, revenue is expected to swell at a CAGR of 2.9% to $27.1 billion over the five years to 2030.

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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.

Description: This dataset contains financial information for companies listed on major stock exchanges around the world, as provided by Yahoo Finance. The data covers a range of industries and includes key financial metrics such as price, volume, market capitalization, P/E ratio, and more.

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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.

Yahoo Finance dataset provides information on top traded companies. It contains financial information on each company including stock ticker and risk scores and general company information such as company location and industry. Each record in the dataset is a unique stock, where multiple stocks can be related to the same company. Yahoo Finance dataset attributes include: company name, company ID, entity type, summary, stock ticker, currency, earnings, exchange, closing price, previous close, open, bid, ask, day range, week range, volume, and much more.

Description:

Explore the dynamic landscape of the Indian stock market with this extensive dataset featuring 4456 companies listed on both the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Gain insights into each company's financial performance, quarterly and yearly profit and loss statements, balance sheets, cash flow data, and essential financial ratios. Dive deep into the intricacies of shareholding patterns, tracking the movements of promoters, foreign and domestic institutional investors, and the public.

This dataset is a rich resource for financial analysts, investors, and data enthusiasts. Perform thorough company evaluations, sector-wise comparisons, and predictive modeling. With figures presented in crore rupees, leverage the dataset for in-depth exploratory data analysis, time series forecasting, and machine learning applications. Stay tuned for updates as we enrich this dataset for a deeper understanding of the Indian stock market landscape. Unlock the potential of data-driven decision-making with this comprehensive repository of financial information.

Folder Structure:

• 4492 NSE & BSE Companies
  • Main directory containing data for 4456 NSE and BSE registered companies.
  • Company_name folder
    • Individual folders for each company allowing for easy organization and retrieval.
    • Company_name.csv
      • General company information.
    • Quarterly_Profit_Loss.csv
      • Quarterly financial data.
    • Yearly_Profit_Loss.csv
      • Annual financial data.
    • Yearly_Balance_Sheet.csv
      • Annual balance sheet information.
    • Yearly_Cash_flow.csv
      • Annual cash flow data.
    • Ratios.csv.csv
      • Financial ratios over time.
    • Quarterly_Shareholding_Pattern.csv
      • Quarterly shareholding pattern.
    • Yearly_Shareholding_Pattern.csv
      • Annual shareholding pattern.

File Explanation:

Company_name.csv

- `Company_name`: Name of the company.
- `Sector`: Industry sector of the company.
- `BSE`: Bombay Stock Exchange code.
- `NSE`: National Stock Exchange code.
- `Market Cap`: Market capitalization of the company.
- `Current Price`: Current stock price.
- `High/Low`: Highest and lowest stock prices.
- `Stock P/E`: Price to earnings ratio.
- `Book Value`: Book value per share.
- `Dividend Yield`: Dividend yield percentage.
- `ROCE`: Return on capital employed percentage.
- `ROE`: Return on equity percentage.
- `Face Value`: Face value of the stock.
- `Price to Sales`: Price to sales ratio.
- `Sales growth (1, 3, 5, 7, 10 years)`: Sales growth percentage over different time periods.
- `Profit growth (1, 3, 5, 7, 10 years)`: Profit growth percentage over different time periods.
- `EPS`: Earnings per share.
- `EPS last year`: Earnings per share in the last year.
- `Debt (1, 3, 5, 7, 10 years)`: Debt of the company over different time periods.

Quarterly_Profit_Loss.csv

 - `Sales`: Revenue generated by the company.
 - `Expenses`: Total expenses incurred.
 - `Operating Profit`: Profit from core operations.
 - `OPM %`: Operating Profit Margin percentage.
 - `Other Income`: Additional income sources.
 - `Interest`: Interest paid.
 - `Depreciation`: Depreciation of assets.
 - `Profit before tax`: Profit before tax.
 - `Tax %`: Tax percentage.
 - `Net Profit`: Net profit after tax.
 - `EPS in Rs`: Earnings per share.

Yearly_Profit_Loss.csv

- Same as Quarterly_Profit_Loss.csv, but on a yearly basis.

Yearly_Balance_Sheet.csv

- `Equity Capital`: Capital raised through equity.
- `Reserves`: Company's retained earnings.
- `Borrowings`: Company's borrowings.
- `Other Liabilities`: Other financial obligations.
- `Total Liabilities`: Sum of all liabilities.
- `Fixed Assets`: Company's long-term assets.
- `CWIP`: Capital Work in Progress.
- `Investments`: Company's investments.
- `Other Assets`: Other non-current assets.
- `Total Assets`: Sum of all assets.

Yearly_Cash_flow.csv

- `Cash from Operating Activity`: Cash generated from core business operations.
- `Cash from Investing Activity`: Cash from investments.
- `Cash from Financing Activity`: Cash from financing (borrowing, stock issuance, etc.).
- `Net Cash Flow`: Overall net cash flow.

Ratios.csv.csv

- `Debtor Days`: Number of days it takes to collect receivables.
- `Inventory Days`: Number of days inventory is held.
- `Days Payable`: Number of days a company takes to pay its bills.
- `Cash Conversion Cycle`: Time taken to convert sales into cash.
- `Wor...

The Financial Statement Data Sets below provide numeric information from the face financials of all financial statements. This data is extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL). As compared to the more extensive Financial Statement and Notes Data Sets, which provide the numeric and narrative disclosures from all financial statements and their notes, the Financial Statement Data Sets are more compact.

The information is presented without change from the "as filed" financial reports submitted by each registrant. The data is presented in a flattened format to help users analyze and compare corporate disclosure information over time and across registrants. The data sets also contain additional fields including a company's Standard Industrial Classification to facilitate the data's use.

Content

Each quarter's data is stored as a json of the original text files. This was necessary to limit the overall number of files. The num.txt file will likely be of most interest.

Acknowledgements

This dataset was kindly made available by the SEC. You can find the original dataset, which is updated quarterly, here.

The data contains selected financial statement information for twenty-seven non-financial firms listed on the Zimbabwe Stock Exchange for the period 2010 to 2017.

This dataset contains information about all publications collected from the Scopus database using the keywords of (“data market*”) and (“data marketplace*”). The dataset was extracted on 6 July 2020. The dataset is a supplementary document of the article entitled “Business Data Sharing through Data Marketplaces: A Systematic Literature Review”. The dataset contains nine sheets.

The Corporate Financial Fraud project is a study of company and top-executive characteristics of firms that ultimately violated Securities and Exchange Commission (SEC) financial accounting and securities fraud provisions compared to a sample of public companies that did not. The fraud firm sample was identified through systematic review of SEC accounting enforcement releases from 2005-2010, which included administrative and civil actions, and referrals for criminal prosecution that were identified through mentions in enforcement release, indictments, and news searches. The non-fraud firms were randomly selected from among nearly 10,000 US public companies censused and active during at least one year between 2005-2010 in Standard and Poor's Compustat data. The Company and Top-Executive (CEO) databases combine information from numerous publicly available sources, many in raw form that were hand-coded (e.g., for fraud firms: Accounting and Auditing Enforcement Releases (AAER) enforcement releases, investigation summaries, SEC-filed complaints, litigation proceedings and case outcomes). Financial and structural information on companies for the year leading up to the financial fraud (or around year 2000 for non-fraud firms) was collected from Compustat financial statement data on Form 10-Ks, and supplemented by hand-collected data from original company 10-Ks, proxy statements, or other financial reports accessed via Electronic Data Gathering, Analysis, and Retrieval (EDGAR), SEC's data-gathering search tool. For CEOs, data on personal background characteristics were collected from Execucomp and BoardEx databases, supplemented by hand-collection from proxy-statement biographies.

Czech Republic CZ: Number of Listed Domestic Companies: Total data was reported at 24.000 Unit in 2022. This records an increase from the previous number of 21.000 Unit for 2021. Czech Republic CZ: Number of Listed Domestic Companies: Total data is updated yearly, averaging 20.500 Unit from Dec 1993 (Median) to 2022, with 28 observations. The data reached an all-time high of 92.000 Unit in 1998 and a record low of 3.000 Unit in 1993. Czech Republic CZ: Number of Listed Domestic Companies: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Czech Republic – Table CZ.World Bank.WDI: Financial Sector. Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included.;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.

Identifies exchanges by a 1-2 letter code which can be used to filter llm responses. Designed originally as a source of filtering codes for Finnhub's finance API. Each entry maps the exchange code to exchange details. Includes mic codes, geographical details, operating hours, and references. Minor edits have been made to the source to fill in missing details.

Qatar Stock Exchange: Index: QE All Share Banks and Financial Services Index data was reported at 5,245.510 NA in Dec 2025. This records an increase from the previous number of 5,073.570 NA for Nov 2025. Qatar Stock Exchange: Index: QE All Share Banks and Financial Services Index data is updated monthly, averaging 3,831.270 NA from Jan 2012 (Median) to Dec 2025, with 168 observations. The data reached an all-time high of 5,998.610 NA in Apr 2022 and a record low of 1,938.760 NA in Jan 2012. Qatar Stock Exchange: Index: QE All Share Banks and Financial Services Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Qatar – Table QA.EDI.SE: Qatar Stock Exchange: Monthly.

Switzerland Market Capitalization: SIX Swiss Exchange: SPI: Financial Services data was reported at 37,791.903 CHF mn in Nov 2018. This records a decrease from the previous number of 39,570.789 CHF mn for Oct 2018. Switzerland Market Capitalization: SIX Swiss Exchange: SPI: Financial Services data is updated monthly, averaging 24,754.412 CHF mn from Jan 2001 (Median) to Nov 2018, with 215 observations. The data reached an all-time high of 197,137.540 CHF mn in Jan 2001 and a record low of 5,418.600 CHF mn in Oct 2003. Switzerland Market Capitalization: SIX Swiss Exchange: SPI: Financial Services data remains active status in CEIC and is reported by SIX Swiss Exchange. The data is categorized under Global Database’s Switzerland – Table CH.Z002: SIX Swiss Exchange: Market Capitalization.

Peru BCRP Forecast: Exchange Rate against US$: Non Financial Companies: Current Calendar Year data was reported at 3.320 PEN/USD in Oct 2018. This records an increase from the previous number of 3.300 PEN/USD for Sep 2018. Peru BCRP Forecast: Exchange Rate against US$: Non Financial Companies: Current Calendar Year data is updated monthly, averaging 3.220 PEN/USD from Sep 2001 (Median) to Oct 2018, with 199 observations. The data reached an all-time high of 3.650 PEN/USD in Oct 2002 and a record low of 2.500 PEN/USD in Jan 2013. Peru BCRP Forecast: Exchange Rate against US$: Non Financial Companies: Current Calendar Year data remains active status in CEIC and is reported by Central Reserve Bank of Peru. The data is categorized under Global Database’s Peru – Table PE.M010: Foreign Exchange Rate: Forecast: Central Reserve Bank of Peru.

Stock Exchange Dataset Description

Dataset Overview: The Stock Exchange Dataset provides historical stock market data, capturing essential metrics for financial analysis and market research. This dataset contains detailed information about the performance of various stocks over time.

Key Features:

Date: The trading date corresponding to each data entry.

Opening Price: The price at which the stock starts trading when the market opens.

Closing Price: The final price of the stock at the end of the trading session.

Adjusted Close Price: The closing price of the stock after adjustments for corporate actions like dividends, stock splits, or rights offerings. This metric provides a more accurate reflection of the stock's value over time.

Volume: The number of shares traded during the specified trading session, indicating the stock's liquidity and market activity.

Usage: This dataset is ideal for stock market analysis, forecasting trends, and building machine learning models for price prediction or financial insights. It can be used by data analysts, researchers, and developers for exploratory data analysis, time series modeling, and investment strategies.

Format: Typically available in CSV format with columns representing each key feature.

Note: Data may require cleaning and normalization before analysis, depending on the source and intended use.

Russia MICEX-RTS Moscow Exchange: Year to Date: Interest Receivable data was reported at 336,171.000 RUB th in Dec 2017. This records an increase from the previous number of 330,501.000 RUB th for Sep 2017. Russia MICEX-RTS Moscow Exchange: Year to Date: Interest Receivable data is updated quarterly, averaging 263,964.000 RUB th from Sep 2006 (Median) to Dec 2017, with 46 observations. The data reached an all-time high of 5,008,449.000 RUB th in Dec 2012 and a record low of 0.000 RUB th in Mar 2013. Russia MICEX-RTS Moscow Exchange: Year to Date: Interest Receivable data remains active status in CEIC and is reported by Company Financial Statement. The data is categorized under Russia Premium Database’s Financial Market – Table RU.ZF001: Company Financial Data: MICEX-RTS Moscow Exchange.

This dataset is for a sample of 50 non-financial firms listed on the Johannesburg Stock Exchange.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Techsalerator's Corporate Actions Dataset in Sri Lanka 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 289 companies traded on the Colombo Stock Exchange (XCOL).

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Top 5 used data fields in the Corporate Actions Dataset for Sri Lanka:

• 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.

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Top 5 corporate actions in Sri Lanka:

Equity Issuances: Sri Lankan companies often engage in equity issuances such as initial public offerings (IPOs) or rights issues to raise capital for expansion and growth.

Mergers and Acquisitions (M&A): Corporate actions related to mergers, acquisitions, and takeovers play a role in shaping industry landscapes and consolidation in Sri Lanka.

Foreign Direct Investment (FDI): Corporate actions involving foreign investment and joint ventures contribute to economic growth and international collaboration in various sectors.

Debt Issuances: Sri Lankan businesses may issue bonds or other debt instruments to raise funds for capital investments and operational needs.

Dividend Declarations: Companies often announce dividends to distribute profits to shareholders, reflecting financial performance and commitment to shareholder value.

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Top 5 financial instruments with corporate action Data in Sri Lanka

Colombo Stock Exchange (CSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Colombo Stock Exchange. This index would provide insights into the performance of the Sri Lankan stock market.

Colombo Stock Exchange (CSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Colombo Stock Exchange. This index would give an overview of foreign business involvement in Sri Lanka.

LankaMart: A Sri Lanka-based supermarket chain with operations in multiple regions. LankaMart focuses on offering high-quality products and services to local and international customers.

Inclusive Finance Lanka: A financial services provider with operations across various markets, including Sri Lanka. Inclusive Finance Lanka emphasizes financial inclusion and access to services for underserved populations.

GreenSeed Lanka: A leading producer and distributor of sustainable agricultural products and certified crop seeds in various countries, including Sri Lanka. GreenSeed Lanka contributes to Sri Lanka's commitment to sustainability and responsible agricultural practices.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Sri Lanka, 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 Sri Lanka?

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 Sri Lanka?

Techsalerator provides comprehensive coverage of Corporate Actions Data for various companies and securities traded on the Sri Lanka Stock Exchange. The dataset encompasses major corporate actions announced by entities in the Sri Lanka market.

How does Techsale...

Techsalerator's Corporate Actions Dataset in Australia 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 2200 companies traded on the Australian Securities Exchange* (XASX).

‍

Top 5 used data fields in the Corporate Actions Dataset for Australia:

• 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 Australia:

Resource Sector Developments: Corporate actions in the mining and resource sectors, including new mineral discoveries, expansion of mining operations, and commodity price fluctuations, have a significant impact on Australia's economy.

Financial Services and Fintech: Corporate actions related to financial services, including the growth of fintech companies, digital banking solutions, and changes in financial regulations, play a crucial role in Australia's financial landscape.

Real Estate Investments: Corporate actions in the real estate sector, such as property development projects, commercial real estate investments, and urbanization efforts, are notable contributors to Australia's economy.

Renewable Energy Initiatives: Corporate actions involving investments in renewable energy projects, such as solar and wind farms, reflect Australia's commitment to transitioning to sustainable energy sources.

Healthcare and Biotechnology: Corporate actions in the healthcare and biotechnology sectors, including drug development, medical research, and healthcare technology advancements, are important contributors to Australia's innovation-driven economy.

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Top 5 financial instruments with corporate action Data in Australia

Australian Stock Exchange (ASX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Australian Stock Exchange. This index provides insights into the performance of the Australian stock market.

ASX Foreign Company Index: The index that tracks the performance of foreign companies listed on the Australian Stock Exchange, if foreign listings are present. This index gives an overview of foreign business involvement in Australia.

GroceryLand Australia: An Australia-based supermarket chain with operations in multiple regions. GroceryLand Australia focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinanceDown Under: A financial services provider in Australia with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Australia: A company dedicated to advancing agricultural technology in Australia, focusing on optimizing crop yields, sustainable farming practices, and technological innovation in the agricultural sector.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Australia, 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 Australia?

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 cov...