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...

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

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.

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! 🚀

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

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

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.

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

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...

Discover verified Company Financial Data with Success.ai. Includes profiles of CFOs, financial analysts, and corporate treasurers with work emails and phone numbers. Continuously updated and AI-validated. Best price guaranteed.

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.,

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

China Listed Company: Net Profit: Financial data was reported at 2,669.874 RMB bn in 2024. This records an increase from the previous number of 2,421.360 RMB bn for 2023. China Listed Company: Net Profit: Financial data is updated yearly, averaging 1,251.066 RMB bn from Dec 2001 (Median) to 2024, with 23 observations. The data reached an all-time high of 2,669.874 RMB bn in 2024 and a record low of 1.967 RMB bn in 2001. China Listed Company: Net Profit: Financial data remains active status in CEIC and is reported by China Securities Regulatory Commission. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OZ: Financial Data of Listed Company: Net Profit.

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.

This dataset simulates detailed financial statement records for public and private companies, enriched with fraud risk indicators and audit outcomes. It is designed for developing and benchmarking machine learning models to detect financial statement fraud, with comprehensive fields for financial metrics, suspicious activity counts, and risk scoring. The dataset is ideal for forensic analysis, risk assessment, and audit research in the finance industry.

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.

This paper examines whether firm-specific real options characteristics are equity value-relevant beyond valuation estimates anchored in financial statements. Using extensive historical data for the United Kingdom, we assess and compare the forecast accuracy and explanatory power for stock prices of equity valuation models based on residual income and capitalized earnings against counterparts with add-on real options characteristics. Empirical results show that real options-augmented models enhance forecast accuracy and explanatory power, providing supportive evidence for the value of flexibility from managerial ability to expand, adapt, or abandon. Consistent with real options theory, further evidence shows that the incremental information content of firm-specific real options characteristics is more prominent in high business volatility firms, real options-intensive industries, firms with higher managerial decision-making discretion, and high growth potential firms. Our findings are robust to alternative measurement and sample specifications, accounting for analysts’ forecasts, and several other checks.

This is a dataset that requires a lot of preprocessing with amazing EDA insights for a company. A dataset consisting of sales and profit data sorted by market segment and country/region.

Tips for pre-processing: 1. Check for column names and find error there itself!! 2. Remove '$' sign and '-' from all columns where they are present 3. Change datatype from objects to int after the above two. 4. Challenge: Try removing " , " (comma) from all numerical numbers. 5. Try plotting sales and profit with respect to timeline

TD: LC: Total Liabilities (TL) data was reported at 280,276,898.000 USD th in Dec 2019. This records an increase from the previous number of 271,010,308.000 USD th for Sep 2019. TD: LC: Total Liabilities (TL) data is updated quarterly, averaging 127,897,202.000 USD th from Dec 2000 (Median) to Dec 2019, with 77 observations. The data reached an all-time high of 280,276,898.000 USD th in Dec 2019 and a record low of 4,032,406.000 USD th in Jun 2001. TD: LC: Total Liabilities (TL) data remains active status in CEIC and is reported by Federal Deposit Insurance Corporation. The data is categorized under Global Database’s United States – Table US.KB062: Financial Data: Federal Deposit Insurance Corporation: TD Bank.