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

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

The data sets below provide selected information extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

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...

Financial Statement Analysis Dataset

Dataset Overview

This dataset provides a comprehensive collection of financial statement data from various companies, covering key financial metrics used for financial statement analysis. It includes information from income statements, balance sheets, and cash flow statements, enabling users to perform ratio analysis, trend analysis, and predictive modeling.

Dataset Features

• Company Name & Industry: Identifiers for different companies and their industry classification.
• Fiscal Year & Quarter: Time-based financial reporting periods.
• Income Statement Metrics: Revenue, net income, operating income, gross profit, EPS (Earnings Per Share), etc.
• Balance Sheet Metrics: Total assets, total liabilities, shareholder equity, current assets, current liabilities, etc.
• Cash Flow Statement Metrics: Operating cash flow, investing cash flow, financing cash flow, free cash flow, etc.
• Financial Ratios: Profitability ratios (e.g., ROA, ROE, Gross Margin), liquidity ratios (e.g., Current Ratio, Quick Ratio), solvency ratios (e.g., Debt-to-Equity, Interest Coverage), and efficiency ratios (e.g., Asset Turnover).

Potential Use Cases

• Financial Performance Analysis: Evaluate company profitability, liquidity, and solvency.
• Predictive Modeling: Train machine learning models to predict financial distress or stock performance.
• Investment Research: Identify undervalued or overvalued companies using fundamental analysis.
• Academic Research & Education: Teach financial statement analysis, corporate finance, and machine learning applications in finance.

Source & Disclaimer

The dataset is collected from publicly available financial reports and regulatory filings. Users should verify data accuracy before making financial decisions. This dataset is for educational and research purposes only.

📥 Download, analyze, and gain insights into financial health! 🚀

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

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.

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

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.

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

Introduction

This dataset contains 604 public company financial statement annually in IDX (Bursa Efek Indonesia), largest number that I can see in kaggle :D. Company that's not included in this dataset either do not report their financial statement or contains some irrelevant publishing date.

Usability

• EDA
• Classifier Stock
• Fundamental Analysis
• Financial Statement Analysis

Wanna Contribute?

Please leave a message on suggestions!

Appendix

Type:

Account:

254 Chinese listed companies

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

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

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

Analyst often asked, which method bring higher return? Technical or Fundamental? When should we use techinal analysis or when fundamental analysis is more relevant?. These question sparks a lot of debate and each side assume that they are better than the other. Warren Buffet himself is a value investor, who bought stock depends on how the company perform, and mention that buying a stock based on recent price alone is kind of foolish. Market decide price, and our turn to buy it if the price favor our assumption on how valueable a company is. The intelligent investor buy the business, not the stock. With these financial reports we could perform a small fundamental analysis based on the value on each item in statement. Of course fundamental analysis covers a lot more than just financial statement alone, but this could be a jumping stone to see how the actual price as stock supposed to be. Combine it with the stock price, and we'll proseper.

This dataset contains almost all public company financial statement annually and quarterly. Company that's not included in this dataset either do not report their financial statement or contains some irrelevant publishing date

How to use this dataset

EDA Classifier Stock Recommendation Fundamental Analysis

Features

Other Information

You also could use dataset outside this one. This dataset present all public company data in Indonesia and Stock Price cover the stock price movement of those companies. Might be helpful to do certain task, e.g. classification for the industry, etc.

Acknowledgement

Yahoo Finance

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!

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

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

Financial company financial credit information is data that provides the main financial statements of financial companies based on the base date, corporate registration number, and fiscal year. This data is provided through the following three operations. 1. Financial company summary financial statement inquiry: Summary financial indicators such as sales amount, operating profit, net income for the period, total assets, total capital, and debt ratio. 2. Financial company financial statement inquiry: Amounts by accounting account subject such as total assets, total liabilities, and total capital (including comparisons by current year, previous year, and previous year) 3. Financial company income statement inquiry: Detailed amounts by profit and loss item such as operating revenue, operating profit, and net income (comparison by account subject possible)