This dataset contains important financial information and accounting ratios of the top 200 US Companies. Source of data in Yfiannce

This dataset provides a structured and machine-readable collection of financial statements filed with the Companies Registration Office (CRO) in Ireland. It currently includes financial statements for the year 2022, with additional years to be added as they become available. The dataset aligns with the European Union’s Open Data Directive (Directive (EU) 2019/1024) and the Implementing Regulation (EU) 2023/138, which designates company and company ownership data as a high-value dataset. It is available for bulk download and API access under the Creative Commons Attribution 4.0 (CC BY 4.0) licence, allowing unrestricted reuse with appropriate attribution. By increasing transparency and enabling data-driven insights, this dataset supports public sector initiatives, financial analysis, and digital services development. The API endpoints can be accessed using these links - Query - https://opendata.cro.ie/api/3/action/datastore_search Query (via SQL) - https://opendata.cro.ie/api/3/action/datastore_search_sql

The data sets provide the text and detailed numeric information in all financial statements and their notes extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

Context

This is a comprehensive dataset including numerous financial metrics that many professionals and investing gurus often use to value companies. This data is a look at the companies that comprise the S&P 500 (Standard & Poor's 500). The S&P 500 is a capitalization-weighted index of the top 500 publicly traded companies in the United States (top 500 meaning the companies with the largest market cap). The S&P 500 index is a useful index to study because it generally reflects the health of the overall U.S. stock market. The dataset was last updated in July 2020.

Content

There are 14 rows included in this dataset: ``` - 4 character variables: - Symbol: Ticker symbol used to uniquely identify each company on a particular stock market - Name: Legal name of the company - Sector: An area of the economy where businesses share a related product or service - SEC Filings: Helpful documents relating to a company

- 10 numeric variables:
  - Price: Price per share of the company
  - Price to Earnings (PE): The ratio of a company’s share price to its earnings per share
  - Dividend Yield: The ratio of the annual dividends per share divided by the price per share
  - Earnings Per Share (EPS): A company’s profit divided by the number of shares of its stock
  - 52 week high and low: The annual high and low of a company’s share price
  - Market Cap: The market value of a company’s shares (calculated as share price x number of shares)
  - EBITDA: A company’s earnings before interest, taxes, depreciation, and amortization; often used as a proxy for its profitability
  - Price to Sales (PS): A company’s market cap divided by its total sales or revenue over the past year
  - Price to Book (PB): A company’s price per share divided by its book value

### Acknowledgements

I found this data on the website datahub at https://datahub.io/core/s-and-p-500-companies-financials/r/1.html. All references and citations should be given to them.


### Inspiration

What useful information can you gleam from this dataset? Are these fundamentals enough to predict a high-quality company? How can you determine high from low quality? What would you liked to have seen in this dataset?

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

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The Financial Statements of U.S. Nonbank Subsidiaries of U.S. Holding Companies (FR Y-11; FR Y-11S) reporting forms collect financial information for individual nonfunctional regulated U.S. nonbank subsidiaries of domestic holding companies, which is essential for monitoring the subsidiaries' potential impact on the condition of the holding company or its subsidiary banks. Holding companies file the FR Y-11 on a quarterly or annual basis or the FR Y-11S on an annual basis, predominantly based on whether the organization meets certain asset size thresholds. The FR Y-11 data are used with other holding company data to assess the condition of holding companies that are heavily engaged in nonbanking activities and to monitor the volume, nature, and condition of their nonbanking operations.

This dataset is from the SEC's Financial Statements and Notes Data Set.
It was a personal project to see if I could make the queries efficient.
It's just been collecting dust ever since, maybe someone will make good use of it.
Data is up to about early-2024.
It doesn't differ from the source, other than it's compiled - so maybe you can try it out, then compile your own (with the link below).
Dataset was created using SEC Files and SQL Server on Docker.
For details on the SQL Server database this came from, see: "dataset-previous-life-info" folder, which will contain: - Row Counts - Primary/Foreign Keys - SQL Statements to recreate database tables - Example queries on how to join the data tables. - A pretty picture of the table associations. Source: https://www.sec.gov/data-research/financial-statement-notes-data-sets

Happy coding!

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes summary financial information. The dataset consists of information from the statement of net assets and the statement of revenues, expenses and change in net assets reported by Local Development Corporations that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Dataset Financial Report of 437 Company in Indonesia

Dataset Summary This dataset provides monthly synthetic financial statement data for McDonald's Corporation, spanning from January 2005 to December 2024 (20 years, 240 rows). The structure and field types closely follow actual historical reports, but all values are artificially generated to simulate realistic trends, growth, and variability in key financial metrics.

Disclaimer: This dataset is synthetic and was programmatically generated for educational and analytical purposes. It does not reflect actual financial results of McDonald's.

Columns & Descriptions Column Name Description Date Month of the record (YYYY-MM) Market cap ($B) Market capitalization (billion USD) Revenue ($B) Revenue (billion USD) Earnings ($B) Earnings/Net income (billion USD) P/E ratio Price-to-Earnings ratio P/S ratio Price-to-Sales ratio P/B ratio Price-to-Book ratio Operating Margin (%) Operating margin percentage EPS ($) Earnings per share (USD) Shares Outstanding ($B) Shares outstanding (in billions) Cash on Hand ($B) Cash on hand (billion USD) Dividend Yield (%) Dividend yield percentage Dividend (stock split adjusted) ($) Dividend per share, adjusted for splits (USD) Net assets ($B) Net assets (billion USD) Total assets ($B) Total assets (billion USD) Total debt ($B) Total debt (billion USD) Total liabilities ($B) Total liabilities (billion USD)

Data Generation Synthetic Approach: All values are programmatically generated to simulate plausible historical trends and volatility, based on actual McDonald's data structure and real-world financial logic.

Monthly Granularity: Data points are provided for every month, offering high temporal resolution suitable for time-series analysis.

No Real Data: No actual McDonald's confidential or proprietary data is included.

Example Use Cases Financial time series modeling & forecasting

Data visualization practice

Building dashboards and BI demos

Educational purposes (finance, data science, statistics)

Benchmarking financial data analysis algorithms

Acknowledgements Dataset inspired by public McDonald's annual financial reports.

Get detailed Nasdaq Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: All Manufacturing: Net Sales, Receipts, and Operating Revenues (QFR101MFGUSNO) from Q4 2000 to Q2 2025 about operating, receipts, revenue, finance, Net, corporate, sales, manufacturing, industry, and USA.

Dataset

This dataset was created by Kumagawa AN

Contents

Dataset

This dataset was created by Khalid Ashik

Released under Apache 2.0

Contents

Get detailed Automatic Data Processing Financial Statements 2021-2025. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

The bankruptcy financial company income statement information is data that allows you to check profitability and operating performance based on the income and loss items of a financial company undergoing bankruptcy procedures. It provides item codes, item names, current amount, previous amount, and financial statement classification (operating revenue, operating expenses, corporate tax expenses, etc.) based on the base date and company name. The data consists of a single operation, and the details are as follows. ① Bankruptcy financial company income statement inquiry: Bankruptcy financial company income statement inquiry function that searches report code names, account subject names, current account subject amounts, etc. through the base date, corporate registration number, and fiscal year. This data can be used to understand the management performance flow of a bankrupt financial company, whether it is in deficit, and its accounting performance structure.

Dataset

This dataset was created by AMIT KEDIA

Released under Apache 2.0

Contents

Get detailed Ford Motor Company Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

Annual reports Assessment Dataset

This dataset will help investors, merchant bankers, credit rating agencies, and the community of equity research analysts explore annual reports in a more automated way, saving them time.

Following Sub Dataset(s) are there :

a) pdf and corresponding OCR text of 100 Indian annual reports These 100 annual reports are for the 100 largest companies listed on the Bombay Stock Exchange. The total number of words in OCRed text is 12.25 million.

b) A Few Examples of Sentences with Corresponding Classes The author defined 16 widely used topics used in the investment community as classes like:

Accounting Standards

Accounting for Revenue Recognition

Corporate Social Responsbility

Credit Ratings

Diversity Equity and Inclusion

Electronic Voting

Environment and Sustainability

Hedging Strategy

Intellectual Property Infringement Risk

Litigation Risk

Order Book

Related Party Transaction

Remuneration

Research and Development

Talent Management

Whistle Blower Policy

These classes should help generate ideas and investment decisions, as well as identify red flags and early warning signs of trouble when everything appears to be proceeding smoothly.

ABOUT DATA ::

"scrips.json" is a json with name of companies "SC_CODE" is BSE Scrip Id "SC_NAME" is Listed Companies Name "NET_TURNOV" is Turnover on the day of consideration

"source_pdf" is folder containing both PDF and OCR Output from Tesseract "raw_pdf.zip" contains raw PDF and it can be used to try another OCR. "ocr.zip" contains json file (annual_report_content.json) containing OCR text for each pdf. "annual_report_content.json" is an array of 100 elements and each element is having two keys "file_name" and "content"

"classif_data_rank_freezed.json" is used for evaluation of results contains "sentence" and corresponding "class"

The Consolidated Financial Statements (CFS) since 1995-96 are available on the Department of Finance website at: \r http://www.finance.gov.au/publications/commonwealth-consolidated-financial-statements.\r \r The CFS for the Australian Government present the whole of government and general government sector (GGS) financial reports and are prepared in accordance with AASB 1049 Whole of Government and General Government Sector Financial Reporting. They are required by section 48 of the Public Governance, Performance and Accountability Act 2013 (formerly section 54 of the Financial Management and Accountability Act 1997).\r \r The CFS include the consolidated results for all Australian Government controlled entities as well as disaggregated information on the sectors of GGS, public non financial corporations and public financial corporations. \r \r This dataset provides an historical series of a collection of published CFS for the whole of government and GGS from 2008-09, including the: \r \r • Income Statement\r \r • Balance Sheet \r \r • Cash Flow Statement\r \r The Historical CFS series is provided to assist those who wish to access and analyse this data. \r \r Please note that this dataset represents published information and will not be recast. Figures may not be directly comparable over time due to changes of classification, accounting standards or budget treatments. \r \r This data is released by the Department of Finance.\r