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Quantitative Screening — Quantitative Screening is the initial phase of comparable company selection where database filters are applied to narrow a large population to a manageable set of potential comparables.
Quantitative Screening is the initial phase of comparable company selection where database filters are applied to narrow a large population to a manageable set of potential comparables. Quantitative criteria—such as industry codes, geographic location, independence indicators, and revenue thresholds—reduce thousands of companies to hundreds that can then undergo manual review for functional comparability.
Quantitative screening is objective and database-driven, setting the stage for the subjective manual screening that follows.
The OECD Transfer Pricing Guidelines (2022) address quantitative screening as part of the search process in Chapter III. The Guidelines note that database searches are only one part of identifying potential comparables—it is important not to over-rely on quantitative criteria for comparable selection.
The Guidelines emphasize that quantitative filters are a starting point—manual review remains essential to assess actual comparability.
Standard Quantitative Screening Filters:
| Filter | Purpose | Typical Criteria |
|---|---|---|
| Industry (NACE/SIC) | Identify companies in same sector | Primary 4-digit code + related codes |
| Geography | Limit to relevant markets | Country, region, or economic zone |
| Independence | Exclude controlled entities | BvD indicator A, B (sometimes C) |
| Revenue/Size | Ensure comparable scale | Often 10x-0.1x tested party size |
| Data Availability | Ensure usable financials | 3+ years of complete data |
| Profitability | Exclude persistent loss-makers | Exclude companies with 3+ years losses |
| Active Status | Exclude defunct entities | Currently operating companies |
Recommended Filter Sequence:
Balance Breadth and Specificity: Starting too narrow may exclude valid comparables. Starting too broad creates excessive manual screening burden. Adjust filters iteratively—if results are too few, loosen criteria; if too many, tighten.
Tested Party: Contract manufacturer of automotive parts in Germany
Quantitative Screening Progression:
| Step | Filter Applied | Companies Remaining |
|---|---|---|
| 1 | Database (Orbis): All companies | ~400 million |
| 2 | NACE 29.32 (motor vehicle parts) | 45,000 |
| 3 | Geography: EU-27 | 12,000 |
| 4 | Independence: BvD A, B, B+ | 8,500 |
| 5 | Revenue: €5M–€500M | 1,800 |
| 6 | Data: 3+ years financials | 850 |
| 7 | Exclude persistent losses | 780 |
| 8 | Ready for manual screening | 780 |
After Manual Screening: 12-18 accepted comparables
| Aspect | Quantitative Screening | Manual Screening |
|---|---|---|
| Nature | Objective, database-driven | Subjective, judgment-based |
| Purpose | Reduce population to manageable set | Confirm functional comparability |
| Criteria | Industry, geography, size, independence | Functions, assets, risks, business model |
| Automation | Fully automated | Requires human review |
| Output | Hundreds of candidates | Final comparable set (10-20) |
Theoretically yes, but it's impractical. Without quantitative filters, you'd need to manually review thousands of companies. Quantitative screening efficiently reduces the population to a manageable set. Document your screening criteria to demonstrate a systematic, replicable process.
Broaden your criteria: expand geographic scope, loosen size restrictions, include related industry codes, accept more years of data. If still insufficient, consider whether your functional profile is too narrow or the market genuinely lacks comparable companies.
Tighten your criteria: narrow industry codes, restrict geography, apply stricter size bounds, require more years of data. Alternatively, proceed to manual screening—the extra work may be worthwhile to ensure no good comparables are excluded.
Not necessarily. Quantitative screening typically excludes companies with persistent, unexplained losses (3+ consecutive years). Single-year losses may be acceptable—save detailed evaluation for manual screening. The goal is eliminating clearly non-comparable companies, not making final comparability judgments.
Create a screening matrix showing: (1) database used, (2) each filter applied in sequence, (3) number of companies at each step, (4) final count entering manual screening. This audit trail demonstrates systematic, replicable methodology and protects against claims of cherry-picking.
No—criteria should be tailored to the tested party's functional profile. A small contract manufacturer requires different size and industry filters than a large distributor. Use the tested party's characteristics to define appropriate screening parameters.
Major databases include: Orbis/TP Catalyst (Bureau van Dijk) for global coverage, Amadeus for Europe, Compustat for US public companies, OneSource (Moody's), and regional databases (JADE for Japan, Mint for India). Database selection depends on geography and tested party profile.