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).

CN: Listed Company: Total Revenue: Utility data was reported at 2,322.053 RMB bn in 2024. This records an increase from the previous number of 2,315.544 RMB bn for 2023. CN: Listed Company: Total Revenue: Utility data is updated yearly, averaging 1,154.181 RMB bn from Dec 2012 (Median) to 2024, with 13 observations. The data reached an all-time high of 2,322.053 RMB bn in 2024 and a record low of 699.756 RMB bn in 2012. CN: Listed Company: Total Revenue: Utility 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: Total Revenue.

Our comprehensive and advanced database is completed with all the information you need, with up to >1.5 million company records at your disposal. This allows you to easily perform company search on company profile and company directory, with 99% coverage in Malaysia. Our database also helps you save time so you can focus on your core business activities as company information can be easily accessed through our database.

Our database also contains company profiles on private limited or limited companies globally, including information such as shareholders and financial accounts can be accessed instantly.

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.

CN: Listed Company: Total Revenue: Resident Service data was reported at 0.476 RMB bn in 2024. This records a decrease from the previous number of 0.682 RMB bn for 2023. CN: Listed Company: Total Revenue: Resident Service data is updated yearly, averaging 0.312 RMB bn from Dec 2018 (Median) to 2024, with 7 observations. The data reached an all-time high of 0.682 RMB bn in 2023 and a record low of 0.293 RMB bn in 2022. CN: Listed Company: Total Revenue: Resident Service 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: Total Revenue.

WVB’s Global Fundamentals Dossier provides over 200 standardized financial and non-financial data points for 76,000 active public companies across 219 countries. Data is harmonized across accounting standards, industries, and geographies, enabling seamless global comparative analysis.

Data Attributes • Coverage: 76,000+ global public industrial companies • Geographies: 219 countries, including Asia, Europe, MENA, and North America • History: Up to 10 years of annual, interim, and quarterly data • Data Items: 200+ harmonized fundamental metrics and ratios • Statement Periods: Annual and quarterly • Sources: Audited company reports, interim filings, and official publications

Data Includes Financial Information • Income statement, Balance sheet, and Cash flow. • Key financial ratios and growth metrics • Sales breakdowns by business line and geography (where available)

Non-Financial Information • Company overview and business description • Current executives, directors, and auditors • Key competitors and major shareholders

Key Benefits Standardized financials across an array of markets: Supports robust comparative analysis of companies worldwide. • Analyst-verified: Sourced from audited filings, each report is meticulously curated by WVB’s regional data specialists. • Global Coverage: Includes comprehensive data on listed industrial companies across all regions. • Data Insights: Provides up to 10 years of historical data for trend analysis and performance evaluation.

Ideal For: • Banks (e.g., credit risk, investment research & corporate finance) • Academic institutions • Corporations • Government Institutions • Third-party SaaS platforms

Note: For Financial Institution data, please refer to WVB’s Bank Trader database.

The WVB Analysis Platform is a comprehensive, web-based company analysis solution providing independent, unbiased, and conviction-grade data on companies and financial institutions worldwide. Meticulously collected and verified since 1984, WVB delivers trusted intelligence designed for investment professionals, risk teams, corporate strategists, regulators, and researchers. Comprehensive Global Coverage WVB captures the entire corporate landscape, offering unparalleled depth across: • 76,000+ active publicly listed companies across 219 countries • 6.6 million active private companies, with strong emerging-market coverage • 21,000+ banks and non-bank financial institutions (NBFIs) • Extensive historical records of inactive, delisted, bankrupt, and acquired entities Together, this represents coverage of 99% of total global market capitalization .

Deep Company Financials & Segmentation Data Users gain access to as-reported and harmonized financial statements, including: • Income statements, balance sheets, and cash flows • Up to 40 years of historical financial data • Business and geographic revenue segmentation • Consolidated and unconsolidated accounts • Industry-specific ratios for corporates, banks, and insurers This structure enables accurate cross-border comparability, peer benchmarking, and long-term trend analysis.

Filings, Ownership & Corporate Intelligence The platform integrates essential non-financial and disclosure data such as: • Regulatory filings and source financial reports • Corporate ownership structures and subsidiaries • Directors, officers, shareholders, and PEPs • Dividends, bonds, earnings calls, and corporate actions • ESG, governance, and sustainability disclosures All data is traceable to original sources, ensuring auditability and compliance readiness

Advanced Analytics, Risk Models & AI WVB goes beyond raw data with powerful analytics and modeling tools, including: • Proprietary credit risk and business risk scores • Bankruptcy and financial strength models (Altman Z-Score, Piotroski F-Score, VAIC) • Forecasting, valuation, and stress-testing models • Portfolio analysis, peer comparison, and market aggregates

WVB AI Suite, enabling document summarization, conversational search, and AI-generated analytical reports from filings and transcripts These tools transform raw data into actionable, decision-ready insights .

Built for Professional Workflows Designed to support enterprise-grade use cases, WVB Analysis enables: • Investment research and due diligence • Credit and counterparty risk assessment • Financial crime prevention and KYC • Market and competitive intelligence • Transfer pricing and regulatory compliance • Data extraction, API feeds, and system integration

Why WVB Analysis • Independent and unbiased — no external stakeholder influence • Owned, verified data sourced directly from original filings • Historical depth including inactive and dormant entities • 99.91% accuracy rate with rapid update cycles • Built by financial analysts, for financial professionals

WVB Analysis Platform is not just a database — it is a trusted foundation for global financial analysis, delivering clarity, transparency, and confidence in an increasingly complex data environment

The Financial Statement Data Sets below provide the numeric information from the face financials of all financial statements. This data is extracted from corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL). The Financial Statement Data Sets are more compact compared to other data sets that include additional disclosures.

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.

Data sets will be updated quarterly. Data contained in documents filed after the last business day of a quarter will be included in the subsequent quarterly posting.

The Financial Statements Data (PDF, 305 KB) provides documentation of scope, organization, file formats and table definitions.

DISCLAIMER: The Financial Statement Data Sets contain information derived from structured data filed with the Commission by individual registrants as well as Commission-generated filing identifiers. Because the data sets are derived from information provided by individual registrants, we cannot guarantee the accuracy of the data sets. In addition, it is possible inaccuracies or other errors were introduced into the data sets during the process of extracting the data and compiling the data sets. Finally, the data sets do not reflect all available information, including certain metadata associated with Commission filings. The data sets are intended to assist the public in analyzing data contained in Commission filings; however, they are not a substitute for such filings. Investors should review the full Commission filings before making any investment decision.

Note:

In December 2024, reprocessed Financial Statement Data Sets were posted. The Financial Statements Data Sets were refreshed to only include the submissions and the numeric data from the primary financial statements as rendered by the Commission. Previously, the data sets were compiled to present only data that was applicable to the entire filing entity (non-dimensional) or for a co-registrant. The reprocessed files used rendering data to determine which data points were presented on the primary financial statements. Future data sets will be processed in this manner. The layout and fields remain the same apart from the NUM file where a new field ‘segments’ has been added. The documentation file has been updated to reflect the changes and other clarification or corrections needed. The prior version of the financial statement data sets will be archived but will not be updated.

CN: Listed Company: Total Revenue: Mining data was reported at 8,899.829 RMB bn in 2024. This records a decrease from the previous number of 9,113.499 RMB bn for 2023. CN: Listed Company: Total Revenue: Mining data is updated yearly, averaging 6,728.764 RMB bn from Dec 2012 (Median) to 2024, with 13 observations. The data reached an all-time high of 9,545.366 RMB bn in 2022 and a record low of 4,500.566 RMB bn in 2016. CN: Listed Company: Total Revenue: Mining 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: Total Revenue.

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.

Overview This dataset contains structured balance sheet data of public companies listed on Borsa İstanbul (BIST) between 2008 and 2024, sourced directly from KAP (Kamuyu Aydınlatma Platformu – Public Disclosure Platform).

🔍 Context & Purpose Public companies in Turkey are required to disclose their financials via KAP. However, these reports are often published in non-standard formats (e.g., PDF or HTML tables), making them difficult to analyze programmatically. This dataset aims to make Turkish corporate financial data more accessible and ready-to-use for data science, financial modeling, machine learning, and academic research.

📥 Source All data has been collected from https://kap.org.tr, using publicly available disclosures and financial statements, including: - Annual and quarterly balance sheets - Company-level data from multiple sectors - Cleaned and transformed into a structured, tabular format

📊 Dataset Contents

Each row represents a financial snapshot of a public company. Key columns include:

💡 Use Cases - Financial analysis and time-series forecasting - Credit risk modeling and investment strategies - Academic studies on Turkish capital markets - Machine learning tasks (e.g., clustering, regression)

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!

Historical financial statements for Tractor Supply Company over 17 years, including Income Statement, Balance Sheet, and Cash Flow metrics.

Slavneft-Megionneftegaz: ytd: Other Operating Revenue data was reported at 837,604.000 RUB th in Jun 2018. This records an increase from the previous number of 495,761.000 RUB th for Mar 2018. Slavneft-Megionneftegaz: ytd: Other Operating Revenue data is updated quarterly, averaging 3,033,951.500 RUB th from Sep 2003 (Median) to Jun 2018, with 60 observations. The data reached an all-time high of 34,157,464.000 RUB th in Dec 2011 and a record low of 213,272.000 RUB th in Mar 2005. Slavneft-Megionneftegaz: ytd: Other Operating Revenue data remains active status in CEIC and is reported by Company Financial Statement. The data is categorized under Russia Premium Database’s Mining and Quarrying Sector – Table RU.BAJ011: Company Financial Data: Crude Oil: Slavneft-Megionneftegaz.

TikTok, known as Douyin in its home market, was launched in China in September 2016. It quickly started to gain traction in China and parent company ByteDance launched an international version the...

YouTube was launched in 2005. It was founded by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim, who ran the company from an office above a small restaurant in San Mateo. The first...

Slavneft-Megionneftegaz: ytd: Profit (Loss) before Taxes data was reported at 8,619,172.000 RUB th in Jun 2018. This records an increase from the previous number of 2,843,866.000 RUB th for Mar 2018. Slavneft-Megionneftegaz: ytd: Profit (Loss) before Taxes data is updated quarterly, averaging 7,977,028.000 RUB th from Sep 2003 (Median) to Jun 2018, with 60 observations. The data reached an all-time high of 29,205,945.000 RUB th in Dec 2006 and a record low of -2,806,123.000 RUB th in Dec 2014. Slavneft-Megionneftegaz: ytd: Profit (Loss) before Taxes data remains active status in CEIC and is reported by Company Financial Statement. The data is categorized under Russia Premium Database’s Mining and Quarrying Sector – Table RU.BAJ011: Company Financial Data: Crude Oil: Slavneft-Megionneftegaz.

Historical financial statements for CONMED Corporation over 16 years, including Income Statement, Balance Sheet, and Cash Flow metrics.

Salesforce (CRM) Stock Market Dataset

Context

Salesforce Inc. is one of the world's leading cloud computing and enterprise software companies, best known for its Customer Relationship Management (CRM) platform and cloud-based business solutions.

Founded in 1999 and headquartered in San Francisco, Salesforce has grown into one of the most influential technology companies in the SaaS (Software as a Service) industry. The company provides cloud applications for sales, service, marketing, analytics, and application development.

This dataset contains comprehensive historical stock market data and financial information for Salesforce (NYSE: CRM), covering more than two decades of market activity.

The dataset is structured to support financial research, quantitative trading, machine learning applications, and long-term stock market analysis.

Content

This dataset includes historical stock market data and financial information for Salesforce sourced from multiple financial data providers.

Key characteristics of the dataset include:

• Historical coverage from 2004 to 2026
• 10,000+ daily trading records
• Data from multiple financial providers for validation
• Daily OHLC stock price data
• Trading volume data
• Dividend payment records
• Stock split history
• Company fundamentals and financial statements

The dataset is organized into multiple CSV files allowing both single-source and multi-source analysis.

Files Included

Analysis

This dataset can be used for a wide range of financial and machine learning analyses, including:

• Stock price trend analysis
• Financial time-series forecasting
• Technical indicator development
• Algorithmic trading strategy research
• SaaS sector market analysis
• Portfolio optimization
• Risk and volatility modeling
• Machine learning price prediction

Because the dataset spans more than 20 years of stock market history, it captures multiple economic cycles, technology sector expansions, and market volatility periods.

Variables

Stock Market Variables

Corporate Action Variables

Financial Variables

Financial statement datasets include metrics such as:

• Revenue
• Net income
• Operating income
• Total assets
• Total liabilities
• Shareholder equity
• Free cash flow
• Operating cash flow

Knowledge

This dataset is useful for:

• Financial data science
• Quantitative finance research
• Machine learning price prediction
• SaaS industry market analysis
• Algorithmic trading research
• Academic finance studies
• Investment strategy development

Researchers can combine this dataset with macroeconomic indicators, financial news sentiment data, or other technology sector datasets to develop more advanced predictive models.

This document is part of the source library for NRGI's National Oil Company Database, an open database of facts and figures on more than 70 national oil companies worldwide. See the full database at https://nationaloilcompanydata.org/.