This is a compiled datasets comprising of data from various companies' 10-K annual reports and balance sheets. The data is a longitudinal or panel data, from year 2009-2022(/23) and also consists of a few bankrupt companies to help for investigating factors. The names of the companies are given according to their Stocks. Companies divided into specific categories.

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.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

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

The dataset contains the annual report of US public firms filing with the SEC EDGAR system. Each annual report (10K filing) is broken into 20 sections. Each section is split into individual sentences. Sentiment labels are provided on a per filing basis from the market reaction around the filing data. Additional metadata for each filing is included in the dataset.

Our Financial API provides access to a vast collection of historical financial statements for over 50,000+ companies listed on major exchanges. With this powerful tool, you can easily retrieve balance sheets, income statements, and cash flow statements for any company in our extensive database. Stay informed about the financial health of various organizations and make data-driven decisions with confidence. Our API is designed to deliver accurate and up-to-date financial information, enabling you to gain valuable insights and streamline your analysis process. Experience the convenience and reliability of our company financial API today.

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

Under the Company Financial Reporting in the Queensland Public Sector policy, public sector companies without their own websites must publish their statements on the site of their controlling entity. The following financial statements are for public sector companies controlled by Queensland Treasury.

The Financial Statements of Holding Companies (FR Y-9 Reports) collects standardized financial statements from domestic holding companies (HCs). This is pursuant to the Bank Holding Company Act of 1956, as amended (BHC Act), and the Home Owners Loan Act (HOLA). The FR Y-9C is used to identify emerging financial risks and monitor the safety and soundness of HC operations. HCs file the FR Y-9C and FR Y-9LP quarterly, the FR Y-9SP semiannually, the FR Y-9ES annually, and the FR Y-9CS on a schedule that is determined when this supplement is used.

Korean Companies’ Financial Data provides important information to analyze a company’s financial status and performance. This data includes financial indicators such as revenue, expenses, assets, and liabilities. Collected from corporate financial reports and stock market data, it helps investors evaluate financial health and discover investment opportunities, essential for valuing Korean companies.

Graph and download economic data for Finance Companies; Total Assets (Balance Sheet), Transactions (BOGZ1FU614090610Q) from Q4 1946 to Q1 2025 about balance sheet, finance companies, companies, finance, transactions, financial, assets, and USA.

Comprehensive database of over 100,000 financial filings from 8,000+ European companies

LSEG's Company Data offers an extensive portfolio of content about companies including estimates, filings and ESG. Browse the catalogue.

Custommade Historical Financial Data For 230M Companies Worldwide: - Data from 2017, 2018, 2019, 2020 & 2021 - Includes turnover, employee size. - Custommade based on geographical location, turnover range, employee range and industry type - Standardized database for all countries

Make data work for you. With unbeatable data, skilled data experts and smart technology, we help businesses to unlock the power of international data.

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.

(Formerly Public Accounts: Volume 2)

The Public Accounts of Ontario is a major accountability document which presents the financial statements of the province.

This dataset contains audited financial statements of consolidated organizations and Trusts under Administration.

Starting in 2018-19, Volume 2 is no longer part of the Public Accounts. Find the individual financial statements of government organizations (including hospitals, colleges and school boards), trusts under administration (such as the Workplace Safety and Insurance Board), businesses and other organizations on their websites. "https://www.ontario.ca/page/financial-statements-government-organizations-and-business-enterprises-2019-20">Access listing of organizations (2019-20).

The Financial Administration Act requires the preparation of the Public Accounts for each fiscal year.

The Public Accounts of Ontario are licenced under the Ontario.ca "https://www.ontario.ca/page/terms-use">terms of use (they are not subject to or licenced under the Open Government Licence).

See previous versions of the Public Accounts of Ontario. (Please note that the Public Accounts were only available in PDF format before 2015-16).

*[PDF]: Portable Document Format

Access historical and point-in-time financial statements, ratios, multiples, and press releases, with LSEG's S&P Compustat Database.

Data from 2010 Q1 to 2025 Q1

The data is created with this Jupyter Notebook:

The data format is documented in the Readme. The Sec data documentation can be found here.

Json structure: {"quarter": "Q1", "country": "Italy", "data": {"cf": [{"value": 0, "concept": "A", "unit": "USD", "label": "B", "info": "C"}], "bs": [{"value": 0, "concept": "A", "unit": "USD", "label": "B", "info": "C"}], "ic": [{"value": 0, "concept": "A", "unit": "USD", "label": "B", "info": "C"}]}, "year": 0, "name": "B", "startDate": "2009-12-31", "endDate": "2010-12-30", "symbol": "GM", "city": "York"}

An example Json: {"year": 2023, "data": {"cf": [{"value": -1834000000, "concept": "NetCashProvidedByUsedInFinancingActivities", "unit": "USD", "label": "Amount of cash inflow (outflow) from financing … Amount of cash inflow (outflow) from financing …", "info": "Net cash used in financing activities"}], "ic":[{"value": 1000000, "concept": "IncreaseDecreaseInDueFromRelatedParties", "unit": "USD", "label": "The increase (decrease) during the reporting pe… The increase (decrease) during the reporting pe…", "info": "Receivables from related parties"}], "bs": [{"value": 2779000000, "concept": "AccountsPayableCurrent", "unit": "USD", "label": "Carrying value as of the balance sheet date of … Carrying value as of the balance sheet date of …", "info": "Accounts payable"}]}, "quarter": "Q2", "city": "SANTA CLARA", "startDate": "2023-06-30", "name": "ADVANCED MICRO DEVICES INC", "endDate": "2023-09-29", "country": "US", "symbol": "AMD"}

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.

Graph and download economic data for Finance Companies; Total Assets (Balance Sheet), Transactions (BOGZ1FU614090610A) from 1946 to 2024 about balance sheet, finance companies, companies, finance, transactions, financial, assets, and USA.

Company fundamentals data provides the user with a company's current financial health and when combined historically, the financial 'life-story' of the company.