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).

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

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"

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for namR (ALNMR), provided by FinancialReports.eu.

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

This dataset, titled "Financial-QA-10k", contains 10,000 question-answer pairs derived from company financial reports, specifically the 10-K filings. The questions are designed to cover a wide range of topics relevant to financial analysis, company operations, and strategic insights, making it a valuable resource for researchers, data scientists, and finance professionals. Each entry includes the question, the corresponding answer, the context from which the answer is derived, the company's stock ticker, and the specific filing year. The dataset aims to facilitate the development and evaluation of natural language processing models in the financial domain.

About the Dataset Dataset Structure:

• Rows: 7000
• Columns: 5
• question: The financial or operational question asked.
• answer: The specific answer to the question.
• context: The textual context extracted from the 10-K filing, providing additional information.
• ticker: The stock ticker symbol of the company.
• filing: The year of the 10-K filing from which the question and answer are derived.

Sample Data:

Question: What area did NVIDIA initially focus on before expanding into other markets? Answer: NVIDIA initially focused on PC graphics. Context: Since our original focus on PC graphics, we have expanded into various markets. Ticker: NVDA Filing: 2023_10K

Potential Uses:

Natural Language Processing (NLP): Develop and test NLP models for question answering, context understanding, and information retrieval. Financial Analysis: Extract and analyze specific financial and operational insights from large volumes of textual data. Educational Purposes: Serve as a training and testing resource for students and researchers in finance and data science.

Dataset Financial Report of 437 Company in Indonesia

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.

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for Origin Company, Limited (6513), provided by FinancialReports.eu.

Financial Reporting Software Market size was valued at USD 14.94 Billion in 2024 and is projected to reach USD 37.56 Billion by 2031, growing at a CAGR of 12.81% from 2024 to 2031.

Financial Reporting Software Market Drivers

Regulatory Compliance and Standards: Increasingly complex regulatory requirements and accounting standards necessitate robust financial reporting software. Businesses need tools to ensure compliance with regulations like Sarbanes-Oxley, IFRS, GAAP, and other local financial reporting standards.

Demand for Real-Time Financial Data: Organizations require real-time access to financial data for timely decision-making. Financial reporting software provides real-time data integration, enabling businesses to monitor their financial health and performance continuously.

Automation of Financial Processes: Automation of financial reporting reduces manual errors, saves time, and increases efficiency. Automated reporting tools streamline data collection, processing, and analysis, allowing finance teams to focus on strategic activities.

Context

Understand the influence of a company's financial reports on its stock price

Content

Each line represents a financial report for a given date. For each company there are 4 annual reports with 4 different dates: - 2020-12-31 - 2021-03-31 - 2021-06-30 - 2021-09-30

The columns are : - firm : company name - Ticker : company ticker (the symbol) - Research Development - Income Before Tax - Net Income
- Selling General - Administrative
- Gross Profit
- Ebit
- Operating Income
- Interest Expense
- Income Tax Expense
- Total Revenue - Total Operating Expenses
- Cost Of Revenue
- Total Other Income Expense Net
- Net Income From Continuing Ops
- Net Income Applicable To Common Shares

Acknowledgements

The Data is scrapped from the yahoo finance API.

Inspiration

It could be interesting to analyze the evolution of the features for each company but also to compare the evolution between similar companies (in the same sector for example).

It could also be interesting to couple this dataset with the evolution of the share price for each company and see how the financial report influences the share price.

A kernel with nice visualizations showing the evolution of each of the features would be very instructive

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for for Startups, Inc. (7089), provided by FinancialReports.eu.

Dataset

This dataset was created by Khalid Ashik

Released under Apache 2.0

Contents

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.

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for Rocket Sharing Company (RKT), provided by FinancialReports.eu.

This dataset contains the financial records of 12 major German companies, including top players like Volkswagen AG, Siemens AG, Allianz SE, BMW AG, BASF SE, Deutsche Telekom AG, Daimler AG, SAP SE, Bayer AG, Deutsche Bank AG, Porsche AG, and Merck KGaA. Covering quarterly data from 2017 to 2024, this dataset is designed to provide insights into key financial metrics, allowing for indepth analysis and modeling of corporate financial health, performance, and growth trends. this comprehensive dataset is highly suitable for tasks such as financial forecasting, risk analysis, profitability assessment, and performance benchmarking. Each entry represents one quarter’s financial snapshot for a company, enabling robust time series and cross-sectional analyses.

Data Sources:

Company: Name of the company to which the financial data corresponds (e.g., "Volkswagen AG"). This field categorizes the data and enables cross-company comparisons and individual company trend analysis.

Period: The specific quarter (in year-month format) when the financial data was recorded (e.g., "2017-03-31" for Q1 of 2017). This field is crucial for time-series analysis, allowing users to track financial trends and performance over time.

Revenue: The total revenue of the company for that quarter, measured in billions of Euros. This field provides insight into the company’s sales performance and market reach within each period.

Net Income: The net income (profit after all expenses) of the company for the given quarter, also in billions of Euros. Net income is a key indicator of a company’s profitability and financial efficiency.

Liabilities: The total liabilities (debt and obligations) of the company for the quarter, in billions of Euros. This metric helps gauge the company’s financial leverage and debt exposure, essential for risk assessment.

Assets: The total assets (all owned resources with economic value) for the company in billions of Euros. This metric reflects the scale of the company’s holdings and resources available for operations and investments.

Equity: The shareholder equity calculated as Assets minus Liabilities, in billions of Euros. Equity indicates the residual value owned by shareholders and serves as a core metric for assessing financial stability and value creation.

ROA (%): Return on Assets (ROA), expressed as a percentage, calculated as (Net Income / Assets) * 100. ROA shows how efficiently a company is utilizing its assets to generate profit, an essential measure of operational effectiveness.

ROE (%): Return on Equity (ROE), expressed as a percentage, calculated as (Net Income / Equity) * 100. ROE is a key indicator of financial performance and profitability, reflecting the rate of return on shareholders' investment.

Debt to Equity: The ratio of Liabilities to Equity. This metric provides insights into the company’s capital structure and financial leverage, aiding in risk assessment by showing how much of the company’s operations are funded through debt compared to shareholder equity.

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for ČATEKS d.d. (CTKS), provided by FinancialReports.eu.

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for b-style holdings,Inc. (302A), provided by FinancialReports.eu.