Xverum’s Company Data delivers comprehensive insights into over 50 million global businesses, from fast-growing startups to established private companies. This dataset is a trusted source for investors, analysts, and B2B teams seeking reliable firmographic data, company registry attributes, and organizational details across industries and geographies.

Whether you’re researching potential clients, running B2B campaigns, or building smarter go-to-market strategies, this company dataset gives you the full picture—updated every 30 days.

What’s Included: ✅ 50M+ Verified Company Records across 249 countries ✅ 40+ Firmographic Attributes, including: ✔️ Company Name, Industry ✔️ Employee Count, HQ Location, Founding Year ✔️ Company Domain, Company Profile URL, Registry Type ✅ Private, Public & Startup Coverage with a focus on any business size. ✅ Custom Region Delivery – segment by country, region or worldwide. ✅ 30-Day Refresh Cycle to keep your data fresh and investment-ready ✅ Available in CSV, JSON, or via API & S3

Use Cases: ➡️ Company Research & Competitive Benchmarking Analyze growth metrics and benchmarks across industries and private company peers.

➡️ B2B Lead Generation & Outreach Fuel CRM and outbound sales platforms with firmographic-enriched startup and SMB records.

➡️ Investor Intelligence & Deal Sourcing Spot high-growth startups by tracking employee expansion, market entry, and location-based clusters.

➡️ Market Mapping & Go-To-Market Planning Build total addressable market (TAM) maps using verified business registry records and firmographics.

Why Choose Xverum’s Company Dataset? ✅ Global Reach: 50M companies, with data on startups, SMEs, and private firms in emerging and developed markets ✅ Flexible Formats: Delivered via API, bulk export, or cloud delivery ✅ GDPR & CCPA Compliant: Ethically sourced and privacy-focused

Ready to enrich your CRM or power your next B2B campaign? Request a free sample today or contact us to dive deeper into your data needs.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

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Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data