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

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

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

This dataset offers a detailed and organized set of financial data, enabling users to analyze company performance, conduct stock market research, and develop predictive models. It spans multiple financial aspects, such as annual and quarterly profit and loss statements, balance sheets, cash flow data, financial ratios, and market prices.

The data is structured to support time-series analysis, with datasets covering financial metrics at T0 (financial statements) and T1 (market prices).

This makes it particularly useful for applications requiring cross-temporal insights or forecasting.

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:

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...

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.

At CompanyData.com (BoldData), we specialize in delivering high-quality company data sourced directly from official trade registers. Our extensive dataset includes historical financial records for over 230 million companies worldwide, enabling deeper insight into business performance over time. Whether you're benchmarking companies, training AI models, or building risk profiles, our financial data equips you with the long-term perspective you need.

Our financial database includes multi-year balance sheets, profit and loss statements, and key performance indicators such as revenue, net income, assets, liabilities, and equity. We provide standardized and structured data—backed by rigorous validation processes—to ensure consistency and accuracy across jurisdictions. Each financial profile can be enriched with hierarchical data, firmographics, contact details, and industry classifications to support complex analyses.

This historical financial data supports a wide range of use cases including KYC and AML compliance, credit risk assessment, M&A research, financial modeling, competitive benchmarking, AI/ML training, and market segmentation. Whether you’re building a predictive scoring model or assessing long-term financial health, our data gives you the clarity and depth required for smarter decisions.

Delivery is flexible to suit your needs: access files in Excel or CSV, browse through our self-service platform, integrate via real-time API, or enhance your existing datasets through custom enrichment services. With access to 380 million verified companies across all industries and geographies, CompanyData.com (BoldData) provides the scale, precision, and historical context to power your next move—globally.

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.

FinanceQA Dataset

  📌 Overview

FinanceQA is a curated dataset of financial question-answer pairs extracted from company annual reports, balance sheets, and financial statements. It is designed to support Question Answering (QA), Retrieval-Augmented Generation (RAG), and other NLP applications in financial analysis. The dataset contains ~4,000 entries across multiple companies and years, with structured fields for queries, answers, and contextual excerpts.

  📂… See the full description on the dataset page: https://huggingface.co/datasets/sweatSmile/FinanceQA.

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! 🚀

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"

Success.ai’s Company Financial Data for Banking & Capital Markets Professionals in the Middle East offers a reliable and comprehensive dataset designed to connect businesses with key stakeholders in the financial sector. Covering banking executives, capital markets professionals, and financial advisors, this dataset provides verified contact details, decision-maker profiles, and firmographic insights tailored for the Middle Eastern market.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers your organization to build meaningful connections in the region’s thriving financial industry.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Financial Professionals

   • Access verified email addresses, direct phone numbers, and LinkedIn profiles of banking executives, capital markets advisors, and financial consultants.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing data inefficiencies.

2. Targeted Insights for the Middle East Financial Sector

   • Includes profiles from major Middle Eastern financial hubs such as Dubai, Riyadh, Abu Dhabi, and Doha, covering diverse institutions like banks, investment firms, and regulatory bodies.
   • Gain insights into region-specific financial trends, regulatory frameworks, and market opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, market activities, and organizational structures.
   • Stay ahead of emerging opportunities and align your strategies with evolving market dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible data usage and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers and professionals in banking, investment management, and capital markets across the Middle East.
• 30M Company Profiles: Access detailed firmographic data, including organization sizes, revenue ranges, and geographic footprints.
• Leadership Contact Information: Connect directly with CEOs, CFOs, risk managers, and regulatory professionals driving financial strategies.
• Decision-Maker Insights: Understand key decision-makers’ roles and responsibilities to tailor your outreach effectively.

Key Features of the Dataset:

1. Decision-Maker Profiles in Banking & Capital Markets

   • Identify and connect with executives, portfolio managers, and analysts shaping investment strategies and financial operations.
   • Target professionals responsible for compliance, risk management, and operational efficiency.

2. Advanced Filters for Precision Targeting

   • Filter institutions by segment (retail banking, investment banking, private equity), geographic location, revenue size, or workforce composition.
   • Tailor campaigns to align with specific financial needs, such as digital transformation, customer retention, or risk mitigation.

3. Firmographic and Leadership Insights

   • Access detailed firmographic data, including company hierarchies, financial health indicators, and service specializations.
   • Gain a deeper understanding of organizational structures and market positioning.

4. AI-Driven Enrichment

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

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer financial technology solutions, consulting services, or compliance tools to banking institutions and investment firms.
   • Build relationships with decision-makers responsible for vendor selection and financial strategy implementation.

2. Market Research and Competitive Analysis

   • Analyze trends in Middle Eastern banking and capital markets to guide product development and market entry strategies.
   • Benchmark against competitors to identify market gaps, emerging niches, and growth opportunities.

3. Partnership Development and Vendor Evaluation

   • Connect with financial institutions seeking strategic partnerships or evaluating service providers for operational improvements.
   • Foster alliances that drive mutual growth and innovation.

4. Recruitment and Talent Solutions

   • Engage HR professionals and hiring managers seeking top talent in finance, compliance, or risk management.
   • Provide staffing solutions, training programs, or workforce optimization tools 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 outreach, marketing, and partners...

Financial Training Dataset

A curated dataset for training language models on financial analysis tasks.

  Dataset Description

This dataset contains financial information collected from multiple sources:

Financial news articles Company fundamental data
Market analysis reports RSS financial feeds

  Data Sources

NewsAPI (financial news) AlphaVantage (company fundamentals) Yahoo Finance (market data) Financial RSS feeds

  Dataset Structure

Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/tgishor/financial-training-dataset.

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.

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

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

Dataset Financial Report of 437 Company in Indonesia

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.

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