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

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! 🚀

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

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...

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...

Our comprehensive and advanced database is completed with all the information you need, with up to >1.5 million company financial 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 contains company profiles on private limited or limited companies globally, including information such as shareholders and financial accounts can be accessed instantly.

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.

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...

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.

📊 Coffee Shop Financial Dataset (Synthetic, 2022–2023)

📝 Overview

This dataset simulates the financial records of a small-town coffee shop over a two-year period (Jan 2022 – Dec 2023).
It was designed for data science, bookkeeping, and analytics projects — including financial dashboards, revenue forecasting, and expense tracking.

The dataset contains 5 CSV files representing different business accounts:
1. checking_account_main.csv - Daily sales deposits (hot drinks, cold drinks, pastries, sandwiches) + operating expenses
2. checking_account_secondary.csv - Monthly transfers between accounts + payroll funding
3. credit_card_account.csv - Weekly credit card expenses (supplies, utilities, vendor charges) and payments
4. gusto_payroll.csv - Payroll data for 3 employees + 1 contractor
5. gusto_payroll_bc.csv - Payroll data for 3 full-time employees + 1 contractor + 1 seasonal employee, with actual tax breakdown for the province of British Columbia, Canada

📂 File Details

checking_account_main.csv

• date
• description
• category (Sales, Utilities, Rent, Supplies, etc.)
• amount (positive = inflow, negative = outflow)
• balance

checking_account_secondary.csv

• date
• description
• amount
• balance

credit_card_account.csv

• date
• vendor
• category (Supplies, Marketing, Utilities, etc.)
• amount (negative = charge, positive = payment)
• balance

gusto_payroll.csv

• date
• employee_id
• employee_name (Owner, Barista 1, Barista 2, Contractor)
• role (Owner, Barista, Manager, Contractor)
• gross_pay

gusto_payroll_bc.csv

This file simulates bi-weekly payroll data for a small coffee shop in British Columbia, Canada, covering January 2022 – December 2023.
It reflects realistic Canadian payroll structure with federal and provincial tax breakdowns, CPP, EI, and additional factors.

Columns: - date → Pay date (bi-weekly schedule)
- employee_id → Unique identifier for each employee
- employee_name → Owner, Barista 1, Barista 2, Manager, Contractor, plus a seasonal Barista (June–Aug 2022)
- role → Role within the coffee shop (Owner, Barista, Manager, Contractor)
- gross_pay → Total earnings before deductions (wages + tips + reimbursements)
- federal_tax → Federal income tax withheld
- provincial_tax → British Columbia income tax withheld
- cpp_employee → Employee CPP contribution
- ei_employee → Employee EI contribution
- other_deductions → Placeholder for possible deductions (e.g., garnishments, union dues)
- net_pay → Take-home pay after deductions
- tips → Declared tips (taxable, included in gross pay)
- travel_reimbursement → Non-taxable reimbursement for travel expenses (if applicable)
- cpp_employer → Employer portion of CPP contributions
- ei_employer → Employer portion of EI contributions

Notes: - Payroll data is synthetic but modeled on Canadian payroll rules (2022–2023 rates).
- A seasonal barista employee is included (employed June 1 – Aug 31, 2022).
- Travel reimbursements are non-taxable and recorded separately.
- This file allows users to practice payroll accounting, deductions analysis, and tax reconciliation.

📈 Business Context

• The coffee shop experiences higher sales September–February (holiday season & winter drinks).
• Sales dip March–June due to seasonality in a small town.
• Pastries are sourced from a local bakery, while sandwiches are made in-house.
• Payroll includes 3 employees (baristas, manager) and 1 independent contractor.

🎯 Possible Use Cases

• Build a financial health dashboard
• Forecast revenue and expenses
• Create a profit & loss statement
• Test SQL queries for accounting workflows
• Explore data visualization with Python, R, or BI tools
• Educational projects for small business analytics

📜 License

This dataset is released under the MIT License, free to use for research, learning, or commercial purposes.

⭐ If you use this dataset in your project or notebook, please credit and share your work, it helps the community!

📷 Photo Credits: freepik

The global turnover of the South Korean company known as Lotte Duty Free amounted to just over *** billion euros in 2023. The coronavirus (COVID-19) caused a heavy decline in sales for many travel retail companies (including Lotte Duty Free) in 2020, the effects of which still impacted the industry landscape years later.

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...

The revenue of China Tourism Group Duty Free Corporation Limited with headquarters in China amounted to ***** billion yuan in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately ***** billion yuan. The trend from 2020 to 2023 shows, however, that this increase did not happen continuously.

The Russian Financial Statements Database (RFSD) is an open, harmonized collection of annual unconsolidated financial statements of the universe of Russian firms:

• 🔓 First open data set with information on every active firm in Russia.

• 🗂️ First open financial statements data set that includes non-filing firms.

• 🏛️ Sourced from two official data providers: the Rosstat and the Federal Tax Service.

• 📅 Covers 2011-2023 initially, will be continuously updated.

• 🏗️ Restores as much data as possible through non-invasive data imputation, statement articulation, and harmonization.

The RFSD is hosted on 🤗 Hugging Face and Zenodo and is stored in a structured, column-oriented, compressed binary format Apache Parquet with yearly partitioning scheme, enabling end-users to query only variables of interest at scale.

The accompanying paper provides internal and external validation of the data: http://arxiv.org/abs/2501.05841.

Here we present the instructions for importing the data in R or Python environment. Please consult with the project repository for more information: http://github.com/irlcode/RFSD.

Importing The Data

You have two options to ingest the data: download the .parquet files manually from Hugging Face or Zenodo or rely on 🤗 Hugging Face Datasets library.

Python

🤗 Hugging Face Datasets

It is as easy as:

from datasets import load_dataset import polars as pl

This line will download 6.6GB+ of all RFSD data and store it in a 🤗 cache folder

RFSD = load_dataset('irlspbru/RFSD')

Alternatively, this will download ~540MB with all financial statements for 2023# to a Polars DataFrame (requires about 8GB of RAM)

RFSD_2023 = pl.read_parquet('hf://datasets/irlspbru/RFSD/RFSD/year=2023/*.parquet')

Please note that the data is not shuffled within year, meaning that streaming first n rows will not yield a random sample.

Local File Import

Importing in Python requires pyarrow package installed.

import pyarrow.dataset as ds import polars as pl

Read RFSD metadata from local file

RFSD = ds.dataset("local/path/to/RFSD")

Use RFSD_dataset.schema to glimpse the data structure and columns' classes

print(RFSD.schema)

Load full dataset into memory

RFSD_full = pl.from_arrow(RFSD.to_table())

Load only 2019 data into memory

RFSD_2019 = pl.from_arrow(RFSD.to_table(filter=ds.field('year') == 2019))

Load only revenue for firms in 2019, identified by taxpayer id

RFSD_2019_revenue = pl.from_arrow( RFSD.to_table( filter=ds.field('year') == 2019, columns=['inn', 'line_2110'] ) )

Give suggested descriptive names to variables

renaming_df = pl.read_csv('local/path/to/descriptive_names_dict.csv') RFSD_full = RFSD_full.rename({item[0]: item[1] for item in zip(renaming_df['original'], renaming_df['descriptive'])})

R

Local File Import

Importing in R requires arrow package installed.

library(arrow) library(data.table)

Read RFSD metadata from local file

RFSD <- open_dataset("local/path/to/RFSD")

Use schema() to glimpse into the data structure and column classes

schema(RFSD)

Load full dataset into memory

scanner <- Scanner$create(RFSD) RFSD_full <- as.data.table(scanner$ToTable())

Load only 2019 data into memory

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scanner <- scan_builder$Finish() RFSD_2019 <- as.data.table(scanner$ToTable())

Load only revenue for firms in 2019, identified by taxpayer id

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scan_builder$Project(cols = c("inn", "line_2110")) scanner <- scan_builder$Finish() RFSD_2019_revenue <- as.data.table(scanner$ToTable())

Give suggested descriptive names to variables

renaming_dt <- fread("local/path/to/descriptive_names_dict.csv") setnames(RFSD_full, old = renaming_dt$original, new = renaming_dt$descriptive)

Use Cases

🌍 For macroeconomists: Replication of a Bank of Russia study of the cost channel of monetary policy in Russia by Mogiliat et al. (2024) — interest_payments.md

🏭 For IO: Replication of the total factor productivity estimation by Kaukin and Zhemkova (2023) — tfp.md

🗺️ For economic geographers: A novel model-less house-level GDP spatialization that capitalizes on geocoding of firm addresses — spatialization.md

FAQ

Why should I use this data instead of Interfax's SPARK, Moody's Ruslana, or Kontur's Focus?hat is the data period?

To the best of our knowledge, the RFSD is the only open data set with up-to-date financial statements of Russian companies published under a permissive licence. Apart from being free-to-use, the RFSD benefits from data harmonization and error detection procedures unavailable in commercial sources. Finally, the data can be easily ingested in any statistical package with minimal effort.

What is the data period?

We provide financials for Russian firms in 2011-2023. We will add the data for 2024 by July, 2025 (see Version and Update Policy below).

Why are there no data for firm X in year Y?

Although the RFSD strives to be an all-encompassing database of financial statements, end users will encounter data gaps:

We do not include financials for firms that we considered ineligible to submit financial statements to the Rosstat/Federal Tax Service by law: financial, religious, or state organizations (state-owned commercial firms are still in the data).

Eligible firms may enjoy the right not to disclose under certain conditions. For instance, Gazprom did not file in 2022 and we had to impute its 2022 data from 2023 filings. Sibur filed only in 2023, Novatek — in 2020 and 2021. Commercial data providers such as Interfax's SPARK enjoy dedicated access to the Federal Tax Service data and therefore are able source this information elsewhere.

Firm may have submitted its annual statement but, according to the Uniform State Register of Legal Entities (EGRUL), it was not active in this year. We remove those filings.

Why is the geolocation of firm X incorrect?

We use Nominatim to geocode structured addresses of incorporation of legal entities from the EGRUL. There may be errors in the original addresses that prevent us from geocoding firms to a particular house. Gazprom, for instance, is geocoded up to a house level in 2014 and 2021-2023, but only at street level for 2015-2020 due to improper handling of the house number by Nominatim. In that case we have fallen back to street-level geocoding. Additionally, streets in different districts of one city may share identical names. We have ignored those problems in our geocoding and invite your submissions. Finally, address of incorporation may not correspond with plant locations. For instance, Rosneft has 62 field offices in addition to the central office in Moscow. We ignore the location of such offices in our geocoding, but subsidiaries set up as separate legal entities are still geocoded.

Why is the data for firm X different from https://bo.nalog.ru/?

Many firms submit correcting statements after the initial filing. While we have downloaded the data way past the April, 2024 deadline for 2023 filings, firms may have kept submitting the correcting statements. We will capture them in the future releases.

Why is the data for firm X unrealistic?

We provide the source data as is, with minimal changes. Consider a relatively unknown LLC Banknota. It reported 3.7 trillion rubles in revenue in 2023, or 2% of Russia's GDP. This is obviously an outlier firm with unrealistic financials. We manually reviewed the data and flagged such firms for user consideration (variable outlier), keeping the source data intact.

Why is the data for groups of companies different from their IFRS statements?

We should stress that we provide unconsolidated financial statements filed according to the Russian accounting standards, meaning that it would be wrong to infer financials for corporate groups with this data. Gazprom, for instance, had over 800 affiliated entities and to study this corporate group in its entirety it is not enough to consider financials of the parent company.

Why is the data not in CSV?

The data is provided in Apache Parquet format. This is a structured, column-oriented, compressed binary format allowing for conditional subsetting of columns and rows. In other words, you can easily query financials of companies of interest, keeping only variables of interest in memory, greatly reducing data footprint.

Version and Update Policy

Version (SemVer): 1.0.0.

We intend to update the RFSD annualy as the data becomes available, in other words when most of the firms have their statements filed with the Federal Tax Service. The official deadline for filing of previous year statements is April, 1. However, every year a portion of firms either fails to meet the deadline or submits corrections afterwards. Filing continues up to the very end of the year but after the end of April this stream quickly thins out. Nevertheless, there is obviously a trade-off between minimization of data completeness and version availability. We find it a reasonable compromise to query new data in early June, since on average by the end of May 96.7% statements are already filed, including 86.4% of all the correcting filings. We plan to make a new version of RFSD available by July.

Licence

Creative Commons License Attribution 4.0 International (CC BY 4.0).

Copyright © the respective contributors.

Citation

Please cite as:

@unpublished{bondarkov2025rfsd, title={{R}ussian {F}inancial {S}tatements {D}atabase}, author={Bondarkov, Sergey and Ledenev, Victor and Skougarevskiy, Dmitriy}, note={arXiv preprint arXiv:2501.05841}, doi={https://doi.org/10.48550/arXiv.2501.05841}, year={2025}}

Acknowledgments and Contacts

Data collection and processing: Sergey Bondarkov, sbondarkov@eu.spb.ru, Viktor Ledenev, vledenev@eu.spb.ru

Project conception, data validation, and use cases: Dmitriy Skougarevskiy, Ph.D.,

The sheer volume of financial statements makes it difficult for humans to access and analyze a business's financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions about financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks in the general domain, the finance domain includes complex numerical reasoning and an understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. More details are provided here: Paper, Preview

The dataset is stored as JSON files, each entry has the following format: ``` "pre_text": the texts before the table; "post_text": the text after the table; "table": the table; "id": unique example id. composed by the original report name plus example index for this report.

"qa": { "question": the question; "program": the reasoning program; "gold_inds": the gold supporting facts; "exe_ans": the gold execution result; "program_re": the reasoning program in nested format; } ```

This dataset is the first of its kind intending to enable significant, new community research into complex application domains. It was hosted for a competition at CodaLabs on FinQA where if given a financial report containing both text and table, the goal is to answer a question requiring numerical reasoning. The code is publicly available @GitHub/FinQA

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Dataset

This dataset was created by Irfan Sharif

Released under Data files © Original Authors

Contents

📖 Description

This dataset simulates the financial and ESG (Environmental, Social, and Governance) performance of 1,000 global companies across 9 industries and 7 regions from 2015 to 2025. It contains realistic financial metrics (e.g., revenue, profit margins, market capitalization) alongside comprehensive ESG indicators, including carbon emissions, resource usage, and detailed ESG scores.

Ideal for: - ✅ Regression and classification (e.g., predicting market cap, ESG scores) - ✅ Clustering and segmentation (industry or ESG performance) - ✅ Time-series analysis and forecasting (financial growth, ESG trends) - ✅ Exploring ESG-financial relationships for sustainable investing strategies.

📌 Dataset Details

• Size: 11,000 rows × 16 columns
• Companies: 1,000 unique entities
• Period: Annual data from 2015 to 2025

🗂️ Columns Explained

🔑 Why This Dataset is Unique & Valuable

• ESG and finance intersection: Explore how sustainability impacts corporate performance.
• Synthetic yet realistic: No privacy concerns, fully customizable, with realistic noise and patterns.
• Versatile for ML: Benchmark algorithms on regression, classification, clustering, and forecasting tasks.

📈 Potential Use-Cases

• ESG scoring models
• Financial forecasting and valuation modeling
• Sustainability impact studies
• Investment strategy simulations
• Corporate risk analysis