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...

Symbol: This acts as a unique identifier for a particular stock on a specific exchange. Just like AAPL represents Apple Inc. on the NASDAQ exchange. Name: This is the full name of the company that issued the stock. Currency: This indicates the currency in which the stock is traded. Examples include USD (US Dollar), EUR (Euro), and JPY (Japanese Yen). Exchange: This refers to the stock exchange where the stock is traded. NASDAQ and NYSE are some well-known exchanges. MIC Code: This stands for Market Identifier Code and is used to uniquely identify a specific exchange or trading venue. Country: This specifies the country of incorporation of the company that issued the stock. Type: the type of the st0ck

United States US: Number of Listed Domestic Companies: Total data was reported at 4,336.000 Unit in 2017. This records an increase from the previous number of 4,331.000 Unit for 2016. United States US: Number of Listed Domestic Companies: Total data is updated yearly, averaging 5,930.000 Unit from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 8,090.000 Unit in 1996 and a record low of 4,102.000 Unit in 2012. United States US: Number of Listed Domestic Companies: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Dataset containing over 5000 data metrics (including raw data and BQ calculated scores & metrics) for over 4000 public companies (~95% of the Russell 3000). Includes financials (from SEC filings) as well as data that is not reported to the SEC, including monthly headcount, detailed employee benefits data, credit events related to contributions to benefits plans. Also includes BQ scores, industry and macro statistics that provide a comprehensive view of the sector & industry.

BQ's Public Companies dataset is applicable to both quantitative investment managers as well as fundamentals public equity investors, who wish to use alternative (non-financial) data to enhance their investment analysis and investment decisions.

Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

This dataset is about companies. It has 1 row and is filtered where the company is Man Group. It features 3 columns: website, and market cap.

Dataset Overview

This dataset contains comprehensive stock market data for June 2025, capturing daily trading information across multiple companies and sectors. The dataset represents a substantial collection of market data with detailed financial metrics and trading statistics.

Basic Dataset Information

• Time Period: June 1-21, 2025 (21 trading days)
• Total Records: Approximately 11,600+ entries
• Companies Covered: 500+ unique stocks
• Data Type: Daily stock market trading data with fundamental metrics

Markdown Table Format

Dataset Information Table

Dataset Overview

Data Schema

Market Capitalization Tiers

• Mega Cap (>$1T): 6 companies (AAPL, MSFT, NVDA, AMZN, GOOGL, META)
• Large Cap ($200B-$1T): 28 companies
• Mid Cap ($50B-$200B): 47 companies

Key Market Characteristics

Price Volatility by Sector

• Technology: Higher volatility (±3.5% daily range)
• Energy: High volatility (±4.0% daily range)
• Utilities: Lower volatility (±1.5% daily range)
• Healthcare/Financials: Moderate volatility (±2.5% daily range)

Trading Volume Patterns

• Mega Cap: 25M - 90M shares daily
• Large Cap: 8M - 35M shares daily
• Mid Cap: 2M - 15M shares daily
• Small Cap: 500K - 5M shares daily

Financial Metrics Distribution

• Average P/E Ratio: 25.9 (market-wide)
• Average Dividend Yield: 1.25%
• Price Range: $19 (T) to $3,850 (BKNG)
• EPS Range: $1.50 to $70.00

Notable Market Features

High-Value Stocks

• BKNG (Booking Holdings): $3,650-$3,850 range
• AVGO (Broadcom): $1,650-$1,750 range
• REGN (Regeneron): $1,050-$1,150 range
• LLY (Eli Lilly): $920-$980 range

High-Dividend Yielders

• T (AT&T): 7.1% dividend yield
• VZ (Verizon): 6.2% dividend yield
• PFE (Pfizer): 5.8% dividend yield

Growth & Technology Leaders

• NOW (ServiceNow): P/E ratio of 85
• NVDA (NVIDIA): P/E ratio of 45
• TSLA (Tesla): P/E ratio of 55

Data Quality & Realism Features

✅ Authentic Price Ranges: Based on realistic 2025 market projections ✅ Sector-Appropriate Volatility: Different volatility patterns by industry ✅ Correlated Metrics: P/E ratios, dividend yields, and EPS align with market caps ✅ Realistic Trading Volumes: Volume scaled appropriately to market cap ✅ Temporal Consistency: Logical price progression over 53-day period ✅ Market Cap Accuracy: Daily fluctuations reflect actual price movements

Intended Use Cases

• Financial Analysis & Modeling: Portfolio optimization, risk assessment
• Machine Learning Applications: Predictive modeling, algorithmic trading
• Educational Purposes: Finance courses, data science training
• Algorithm Development: Backtesting trading strategies
• Market Research: Sector analysis, correlation studies
• Visualization Projects: Interactive dashboards, market trend analysis

This dataset provides a comprehensive foundation for quantitative finance research, offering both breadth across market sectors and depth in daily trading dynamics while maintaining statistical realism throughout the observation period...

The main stock market index of United States, the US500, rose to 6718 points on October 3, 2025, gaining 0.04% from the previous session. Over the past month, the index has climbed 3.31% and is up 16.80% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

This private company dataset provides an in-depth view of any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental US.

Also, using robust supply chain data you will be able to map US facilities (including factories, warehouses, and retail outlets).

With this private company dataset, it is possible to track the movement of trucks and devices between locations to identify supply chain connections and company data insights.

Our Machine learning algorithms ingest 7-15bn daily events to estimate the volume of goods transported between locations. Consequently, we can map supply chain connections between:

•Different companies (expressed as a percentage of volume transported).

•Locations owned by the same company (e.g. warehouse to shop).

With this novel geolocation approach, it is possible to "draw" a knowledge graph of any private or public company´s relations with other companies within the country.

This solution, in the form of a dataset, provides an in-depth view of any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental United States.

Use cases:

• Identification and understanding of relations company-to-company: It helps to identify and infer relationships and connections between specific companies or facilities and between sectors/industries.

• Identification and understanding of relations place-to-place: A logistics and domestic distribution supply chain can be mapped, both nationwide and state-wide in the US, and across countries in Europe.

• Visualization and mapping of an entire supply chain network.

• Tracking of products in any distribution or supply chain.

• Risk assessment

• Correlation analysis.

• Disruption analysis.

• Analysis of illicit networks and tracking of illegal use of corporate assets.

• Improvement of casualty risk management.

• Optimization of supply chain risk management.

• Security and compliance.

• Identification of not only the first tier of suppliers in the value chain, but also 2nd and 3rd tier suppliers, and more.

Current largest use case: global corporation using it to model risk at a facility level (+100,000 locations).

Why should you trust PREDIK Data-Driven? In 2023, we were listed as Datarade's top providers. Why? Our solutions for private company data, supply chain data, and B2B data adapt according to the specific needs of companies. Also, PREDIK methodology focuses on the client and the necessary elements for the success of their projects.

This dataset provides financial accouting data for US company stocks along with per-share earnings & price metrics, liquidity ratios, management efficiency measures, margins and stock price data.

Companies are predominantly from the S&P 500 index, with a small number of additions made. The accounting data is on Fiscal Year basis, but most companies have had their stock price sampled up to 3 times in any given year. The time period covers the 10 most recent fiscal years, either 2013-2023 or 2014-2024 depending on when a company's fiscal year ends.

Data was collected from multiple sources, with some fields calculated from various other data points collected. There is no pre-defined target variable, and no directed goal to achieve using this dataset. Please explore and take your own unique approach in terms of how this data can be used, supplementing it with additional data if necessary.

This dataset was created as part of a college research project focused on stock valuation using machine learning, and I am sharing this here so that others may also benefit. I do not intend to maintain this dataset over time. Regardless I do believe that this will be a very valuable and useful dataset for anyone looking to carry out research or just looking to learn more about the area of stock investing using machine learning or other forms of analytics.

This dataset provides a structured and machine-readable register of all companies recorded by the Companies Registration Office (CRO) in Ireland. It includes a daily snapshot of company records, covering both currently registered companies and historical records of dissolved or closed entities. 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. Updated daily, it ensures timely access to corporate information and 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, accountability, and economic innovation, this dataset supports public sector initiatives, research, and digital services development.

this group contains a list of listed companies in Amman stock exchange and their sector , .symbol, code , market and number of shares .

We track hiring activity and employees growth for all US public companies. For each company, we have a link to its Indeed, Glassdoor, and Linkedin profiles, which allows us to understand growth trends in real-time.

The main fields are the number of open job positions, headcount, and various employee ratings (diversity, salary satisfaction, etc.). The dataset has 1 year of history, and the data is updated daily.

This data gives answers to such questions as: - Which companies are most actively hiring right now? - Which companies had the most significant growth of employees over the past week/month/year? - Which companies have the highest rates from employees in terms of ESG, and which ones cannot retain an employee for more than a month?

Such data helps estimate the risks of long-term investing in shares and is valuable for Hedge Funds, M&A firms, and consulting companies.

Access historical and point-in-time financial statements, ratios, multiples, and press releases, with LSEG's S&P Compustat Database.

Coca-Cola (KO) Stock Price Data (1919-2025).

Overview:

This dataset provides historical stock price data for The Coca-Cola Company (NYSE: KO) from September 6, 1919, to January 31, 2025. Extracted from Yahoo Finance, this dataset is valuable for stock market analysis, long-term trend evaluation, and financial modeling.

Dataset Features:

Date: The trading date in YYYY-MM-DD format.

Open: Opening price of Coca-Cola stock on the respective day.

High: Highest price recorded during the trading session.

Low: Lowest price recorded during the trading session.

Close: Closing price of the stock at the end of the trading session.

Adj Close: Adjusted closing price, accounting for stock splits and dividends.

Volume: Total number of shares traded on that day.

Dataset Statistics:

Total Records: 15,877 rows.

Number of Features: 7 columns.

Time Span: Over 100 years of stock data (1919-2025)

Potential Use Cases:

Long-Term Market Trend Analysis – Analyze Coca-Cola’s stock performance over a century. Financial Forecasting – Train machine learning models to predict future stock prices. Volatility Analysis – Assess price fluctuations over different market cycles. Investment Strategy Development – Backtest various trading strategies.

Data Source:

This dataset has been extracted from Yahoo Finance.

License & Citation:

This dataset is publicly available for educational and research purposes. Please cite Yahoo Finance and Muhammad Atif Latif when using it in any analysis.

More Stocks Datasets.

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Market Capitalization for listed companies at the ASE

This file contains a list of the bankruptcy cases for public companies filed under Chapter 11 of the Bankruptcy Code opened and monitored since the fiscal year 2009.

📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

Our dynamic data offering is designed to provide a comprehensive view of over 108,000 publicly listed companies across the globe. This service is an essential tool for financial analysts, investors, corporate strategists, and market researchers, offering versatile data delivery options.

Key Features:

Rich Company Fundamentals: Access detailed profiles with financials, management information, operational metrics, and strategic insights. Historical Data Depth: Utilize our extensive historical data for trend analysis and benchmarking. Flexible Delivery Options: Bulk Data Access: Ideal for high-volume needs, get comprehensive data in bulk. Daily Updates: Stay current with daily data refreshes for timely and relevant insights. API Integration: Seamlessly integrate our data into your systems with our API, ensuring efficient data retrieval and analysis. Global News Integration: Get the latest news and updates, providing context and insights into market movements and company-specific events. Intuitive User Interface: Navigate our platform with ease for efficient data retrieval. Customizable Alerts and Reports: Stay informed with tailored alerts and custom reports. Expert Support: Rely on our dedicated support team for assistance and guidance. Benefits:

Enhance investment strategies with diverse and up-to-date data. Conduct in-depth market research and competitive analysis. Facilitate strategic planning and risk assessment with varied data access methods. Support academic research with a reliable data source. Ideal for:

Investment and Financial Firms Market Analysts and Economists Corporate Strategy and Business Development Teams Academic Researchers in Finance and Economics

Contains stock prices and other details for stocks listed in NEPSE, categorized by date and stock.

All data herein were extracted by web-scraping the official website of the Nepal Stock Exchange (old website). NEPSE official website: http://www.nepalstock.com/

Company details were obtained by web-scraping the webpage at the following link. The data obtained can be found in the "companies_with_details.csv" file. "http://www.nepalstock.com/company">http://www.nepalstock.com/company

Stock Prices and other details for each day starting 2022-06-03 till 2022-07-08 were obtained by web-scraping webpage at the following link. The data obtained can be found in the "By_Date" folder. "http://www.nepalstock.com/todaysprice">http://www.nepalstock.com/todaysprice

Python and BeautifulSoup were used to do the scrapping. 2012-06-03 was used as the start date of data collection because this seems to be the oldest date where data exist at the above link. Non-Traded days have been excluded.

The data obtained thus was further combed through to categorize the data based on individual stocks. The data obtained can be found in the "By_Stock" folder. Note that a few filenames may not match exactly with their company names (as listed). For example, "&" in the listed company name has been replaced with "and" in the stock's filename. Similarly, a '/' in the company name has been replaced with '(underscore)' in the stock's filename. This was done because kaggle does not allow '&' in the filename and Mac OS did not allow '/' in the filename.