Savanet's Russell 1000/2000 datasets include Model Analytics (financial metrics), Model Financials (standardized statements), and Valuation Series (daily multiples), providing comprehensive quantitative and fundamental analysis across US equities.
Savanet Russell 1000 and 2000 Datasets
Model Analytics
The Model Analytics dataset provides sophisticated analytical metrics for Russell 1000/2000 companies, offering quantitative insights across multiple dimensions of financial performance.
SavaNet's Model Analytics is part of their suite of financial modeling and analytics solutions that includes "hyper-detailed company consensus models with three years of full financial statement forecasts" for companies in major indexes. SavaNet | LinkedIn The dataset covers Russell 1000 (large-cap) and Russell 2000 (small-cap) companies with historical data dating back to 1998.
This dataset organizes hundreds of analytics tags into eight primary categories:
- Growth: Tracking changes in metrics like shares outstanding and liability growth
- Leverage: Analyzing company debt structure and asset-to-liability ratios
- Margin: Examining profitability metrics and comparisons to industry peers
- Multiple: Valuation metrics relating market cap to sales, enterprise value, etc.
- Position: Date-based information about reporting periods
- Relvalue: Relative value metrics including price-to-book comparisons
- Turnover: Metrics examining debt management and operational efficiency
- Utilize: Return-based measurements of how effectively assets are being utilized
Each metric includes detailed identification (ISIN, RIC, Sedol) and reporting context (fiscal period, reporting status).
Model Financials
The Model Financials dataset focuses on the comprehensive financial statement data of Russell 1000/2000 companies, providing detailed line-item reporting organized into three primary categories:
SavaNet's Model Financials includes "full Consensus Models™, including complete income statement, balance sheet and cash flow statements" with "daily updates to 9,000 company financial models" organized using "the world's only complete, hierarchical taxonomy." Financial Forecasting Models | Financial Modeling Software | SavaNet
This dataset contains hundreds of standardized financial tags categorized primarily as:
- Position (65%): Balance sheet items showing financial status at specific points in time
- Performance (35%): Income statement items reflecting operational results over time
- Margin (rare): Specific profitability and efficiency metrics
The dataset includes essential financial data points such as release dates, enterprise value components, capital structure, liabilities, debt obligations, equity metrics, revenue figures, income measurements, and share counts. As with the Analytics dataset, each record is fully identified with ISIN, RIC, Sedol codes and appropriate time period markings.
Valuation Series
The Valuation Series dataset provides sophisticated valuation metrics calculated daily, enabling time-series analysis of company valuations across different temporal perspectives.
This dataset "contains an exhaustive set of daily 'rolling' historical point-in-time valuation series using Consensus Models™ for ultra-precise temporal matching of income with balance sheet positions" and includes "20 daily high-order valuation series going back 10 years." Company Valuation Analytics | Equity Quant Factors | Savanet
Each valuation metric is calculated across multiple time horizons:
- LTM (Last Twelve Months): Rolling 365-day period ending on the series date
- NTM (Next Twelve Months): Forward-looking 365-day period starting after the series date
- FTM (Forward Twelve Months): Second year forward (days 366-730 from series date)
- L4Q: Using values from the most recently reported four quarters
The valuation tags are organized into three main prefix categories:
- Interp_multiple (~67%): Price and enterprise value multiples (EV/EBITDA, P/E, etc.)
- Interp_relvalue (~20%): Relative value metrics (price-to-book, PEG ratios, etc.)
- Interp_yield (~12%): Yield-based valuations (dividend yield, cash flow yield, etc.)
While historical data is typically updated monthly, the production data feed uses daily updates based on the current model, providing near real-time valuation analytics.
Together, these three datasets create a powerful suite of quantitative tools for analyzing Russell 1000 and 2000 companies, enabling both fundamental and quantitative investment approaches. SavaNet's approach has revealed interesting insights, such as how "forward-looking NTM cash-based EV/EBITDA and EV/Revenue valuations" have significantly outperformed traditional "accounting-based P/E and P/B valuations over the trailing L4Q period" in certain value investing strategies.