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).

Dataset

This dataset was created by Kumagawa AN

Contents

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.

The statistic presents the leading financial data service companies in the United States in 2015, by revenue. In that year, Visa was ranked second with the revenue of approximately 13.88 billion U.S. dollars.

Success.ai’s Company Financial Data for European Financial Professionals provides a comprehensive dataset tailored for businesses looking to connect with financial leaders, analysts, and decision-makers across Europe. Covering roles such as CFOs, accountants, financial consultants, and investment managers, this dataset offers verified contact details, firmographic insights, and actionable professional histories.

With access to over 170 million verified professional profiles, Success.ai ensures your outreach, market research, and partnership strategies are driven by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is indispensable for navigating the fast-paced European financial landscape.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of financial professionals across Europe.
   • AI-driven validation ensures 99% accuracy, reducing communication inefficiencies and improving engagement rates.

2. Comprehensive Coverage Across Europe

   • Includes financial professionals from key markets such as the United Kingdom, Germany, France, Italy, and the Netherlands.
   • Gain insights into regional financial trends, industry dynamics, and regulatory landscapes.

3. Continuously Updated Datasets

   • Real-time updates capture changes in professional roles, company structures, and market conditions.
   • Stay ahead of industry shifts and capitalize on emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 170M+ Verified Professional Profiles: Access detailed profiles of European financial professionals across industries and sectors.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for precise targeting.
• Firmographic Data: Understand company sizes, revenue ranges, and geographic footprints to inform your outreach strategy.
• Leadership Insights: Connect with CFOs, financial controllers, and investment managers driving financial strategies.

Key Features of the Dataset:

1. Comprehensive Financial Professional Profiles

   • Identify and connect with key players in finance, including financial analysts, accountants, and consultants.
   • Target professionals responsible for budgeting, investment strategies, regulatory compliance, and financial planning.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (banking, fintech, asset management), geographic location, or job function.
   • Tailor campaigns to align with specific financial needs, such as software solutions, advisory services, or compliance tools.

3. Regional and Industry Insights

   • Leverage data on European financial trends, regulatory challenges, and market opportunities.
   • Refine your approach to align with industry-specific demands and geographic preferences.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Lead Generation

   • Design targeted campaigns to promote financial software, advisory services, or compliance solutions to European financial professionals.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media.

2. Partnership Development and Collaboration

   • Build relationships with financial firms, fintech companies, and investment organizations exploring strategic partnerships.
   • Foster collaborations that enhance financial efficiency, innovation, or regulatory compliance.

3. Market Research and Competitive Analysis

   • Analyze financial trends across Europe to refine product offerings, marketing strategies, and business expansion plans.
   • Benchmark against competitors to identify growth opportunities and emerging demands.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for financial roles, from analysts to CFOs.
   • Provide workforce optimization platforms or training solutions tailored to the financial sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality financial data at competitive prices, ensuring strong ROI for your marketing, sales, and partnership initiatives.

2. Seamless Integration

   • Integrate verified financial data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and boost conversion rates in financial ca...

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

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 Quarterly Financial Report: U.S. Corporations: All Information: Interest Expense (QFR105INFUSNO) from Q4 2009 to Q2 2025 about information, finance, expenditures, corporate, interest, industry, and USA.

China Listed Company: Net Profit: Financial data was reported at 2,669.874 RMB bn in 2024. This records an increase from the previous number of 2,421.360 RMB bn for 2023. China Listed Company: Net Profit: Financial data is updated yearly, averaging 1,251.066 RMB bn from Dec 2001 (Median) to 2024, with 23 observations. The data reached an all-time high of 2,669.874 RMB bn in 2024 and a record low of 1.967 RMB bn in 2001. China Listed Company: Net Profit: Financial data remains active status in CEIC and is reported by China Securities Regulatory Commission. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OZ: Financial Data of Listed Company: Net Profit.

Financial Data Services Market size was valued at USD 23.3 Billion in 2023 and is projected to reach USD 42.6 Billion by 2031, growing at a CAGR of 8.1% during the forecast period 2024-2031.Global Financial Data Services Market DriversThe market drivers for the Financial Data Services Market can be influenced by various factors. These may include:The need for real-time analytics is growing: Real-time analytics are becoming more and more necessary in the financial sector due to the acceleration of data consumption. To reduce risks, make wise decisions, and enhance customer service, organizations need quick insights. Stakeholders are giving priority to solutions that enable quick data processing and analysis due to the increase in market volatility and complexity. The need for sophisticated analytical skills is driving providers of financial data services to modernize their products. As companies come to realize that using real-time data is crucial for keeping a competitive edge in a fast-paced financial climate, the competition among them to provide timely insights also boosts market growth.Growing Machine Learning and AI Adoption: Data analysis has been profoundly changed by the incorporation of AI and machine learning technology into financial data services. By enabling predictive analytics, these technologies help financial organizations make better decisions and reduce risk. Businesses can find trends that were previously invisible by automating data processing operations. This leads to more precise forecasts and improved investment plans. Furthermore, sophisticated algorithms are flexible enough to adjust to shifting circumstances, keeping organizations flexible. The increasing intricacy of financial markets necessitates the use of AI and machine learning, which in turn drives demand for sophisticated financial data services and promotes innovation in the sector.Global Financial Data Services Market RestraintsSeveral factors can act as restraints or challenges for the Financial Data Services Market. These may include:Difficulties in Regulatory Compliance: Regulations controlling data management, privacy, and financial transactions place heavy restrictions on the financial data services market. Regulations like the GDPR, CCPA, and banking industry standards like Basel III and SOX must all be complied with by organizations. Complying with these requirements frequently necessitates a significant investment in staff and compliance systems, which can be taxing, especially for smaller businesses. Regulations are dynamic, and different locations have different needs, which adds to the complexity and expense. Noncompliance not only results in monetary fines but also has the potential to harm an entity's image, so impeding market expansion.Dangers to Data Security: Threats to data security are a major impediment to the financial data services market. Because they manage sensitive data, financial institutions are often the targets of cyberattacks. Breach can lead to significant monetary losses, legal repercussions, and long-term harm to one's image. Although they can greatly increase operating expenses, investments in strong security measures like encryption, safe access protocols, and continual monitoring are crucial. Moreover, the dynamic strategies employed by cybercriminals need continuous adjustment, placing a burden on resources and detracting from the main operations of businesses. The evolution of security threats poses a challenge to preserving consumer trust, hence impeding industry expansion.

Corporate financial information is data that allows you to search for corporate financial statement items based on the corporate registration number and fiscal year. The items provided include not only summarized financial information such as the company's sales, operating profit, total assets, total liabilities, and capital, but also financial statements and income statement items by account subject. The data consists of three operations: summary financial statement inquiry, financial statement inquiry, and income statement inquiry, and comparative figures for the previous year, current year, and previous quarter are also provided for each item. This data can be used for various financial analyses such as corporate management performance analysis, financial soundness evaluation, investment risk analysis, and financial comparison between companies.

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!

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McDonald’s Corporation is an American operator and franchisor of fast food restaurants represented worldwide and the biggest fast food company in the world. Founded in 1940 by Richard and Maurice MacDonald, the company has grown significantly and played a key role in shaping the fast food industry. The brand prides itself on providing consistent, fast and affordable food. According to our data, McDonald's is the 51st most valuable company in the world by market capitalization. The company posted revenue of $23.18 billion in 2022, compared to revenue of $23.22 billion in 2021.

Let's take a look at McDonald's financial data together and see if the biggest fast food player in the world is doing so well.

The data contains the following columns:

Year: Year for which the reports were provided.

Market cap: Market capitalization of McDonald. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue: Revenue for McDonald. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings: Earnings for McDonald. The profit shown in this column represents the company's income before taxes.

P/E ratio: P/E ratio for McDonald. The P/E (Price-to-Earnings) ratio, or price-to-earnings ratio, is one of the key indicators of stock financial analysis. It is used to evaluate the relative high or low value of a company's shares in the market.

P/S ratio: P/S ratio for McDonald. P/S (Price-to-Sales) ratio, or price to sales ratio, is another key indicator in stock financial analysis. It is also used to evaluate whether a company's shares are expensive or cheap in the market, but unlike P/E, which uses earnings, P/S uses a company's earnings.

P/B ratio: McDonald - P/B ratio. P/B (Price-to-Book) ratio, is another important indicator of a stock's financial analysis. It is used to evaluate the value of a company's shares relative to their book value, that is, the relationship between the market price of the shares and the book value (expressed as book value) of the shares. In other words, it is a way to measure what the stock market thinks a company is worth, compared to what the company says its assets are worth on paper.

Operating Margin: Operating Margin for McDonald. This is a financial indicator that allows you to evaluate the effectiveness of a company’s activities in the production process or provision of services. This metric measures the percentage of profit a company makes from sales after deducting all operating costs such as raw materials, labor, facility rent, taxes, and other variable and fixed costs.

EPS: EPS for McDonald. EPS (Earnings Per Share) is a metric that measures the net earnings per share of a company's common shareholder. It is widely used by investors and analysts to evaluate the performance of a company's stock.

Shares Outstanding: Number of shares outstanding for McDonald.

Cash on Hand: McDonald - Cash on Hand. A company’s cash on hand also refered as cash/cash equivalents (CCE) and Short-term investments, is the amount of accessible money a business has.

Dividend Yield: Dividend yield history for McDonald.

Dividend (stock split adjusted): Dividend history for McDonald.

Net assets: McDonald - Net assets. A company’s net assets is the sum of its assets minus the sum of its liabilities.

Total assets: McDonald - Total assets. A company’s total assets is the sum of all current and non-current assets, such as inventories, cash and cash equivalents, properties and equipment.

Total debt: McDonald - Total debt. A company’s total debt is the sum of all current and non-current debts.

Total liabilities: McDonald - Total liabilities. A company’s total liabilities is the sum of all current and non-current debts and obligations.

($B) - billion dollar symbol

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

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.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: All Retail Trade: Income (Loss) from Operations (QFR104RETUSNO) from Q4 2000 to Q2 2025 about gains/losses, operating, finance, retail trade, corporate, sales, retail, income, industry, and USA.

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.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: All Manufacturing: Income (Loss) After Income Taxes (QFR115MFGUSNO) from Q4 2000 to Q2 2025 about gains/losses, finance, tax, corporate, income, manufacturing, industry, and USA.

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.

This dataset contains financial information for the top 2000 global companies in 2024. It includes key metrics such as company name, country, sales, profit, assets, and market value. The data provides valuable insights into the financial performance and market presence of leading companies across various industries worldwide. Ideal for financial analysis, visualization, and global market research.