Quantitative Screening Filters for Transfer Pricing Benchmarking: Complete Guide

Quick Answer: What Are Quantitative Screening Filters?

Quantitative screening filters narrow a database of thousands of companies to a manageable set of potential comparables before manual review. The key filters are: industry codes (NACE/SIC/NAICS), geographic location, independence indicators, size thresholds, financial data availability, and profitability requirements.

Apply filters in a logical sequence from broad to narrow—starting with industry and geography, then independence and status, then size and profitability. Document all criteria and the number of companies remaining at each step (per EU JTPF transparency recommendations). A well-executed quantitative screen yields a manageable set of candidates for qualitative review—often a few dozen companies—from which practitioners typically finalize 5-15 comparables.

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Quantitative Filter Catalog (At a Glance)

Use this as a checklist of the most common quantitative filters. Exact thresholds depend on the tested party and data universe—what matters is that each filter is economically motivated and traceable.

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The Role of Quantitative Screening

Quantitative screening is the first step in a comparables search. Its purpose is to filter a database universe—often containing millions of companies—down to a few dozen or hundred candidates that warrant individual examination.

The process uses objective, measurable criteria that databases can apply automatically:

• Industry classification — selecting companies in the same line of business
• Geography — limiting to relevant markets
• Independence — excluding subsidiaries and affiliates
• Size — filtering by revenue, assets, or employees
• Data availability — requiring minimum years of financial data
• Profitability — screening out persistent loss-makers or distressed companies
• Operational status — focusing on active, going-concern businesses

After quantitative filters, the remaining companies undergo manual (qualitative) screening—reviewing business descriptions, functional profiles, and financial details to confirm true comparability.

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Industry Classification Filters

Industry filters ensure only companies engaged in comparable economic activities are considered. They rely on standardized classification codes.

Common Code Systems

Most databases support multiple systems. Orbis, for example, allows searching by NACE Rev.2, NAICS, US SIC, and proprietary BvD sectors.

Depth of Codes: 2-Digit vs. 4-Digit

Best practice: Use the most specific code that captures the tested party's activity—typically 4-digit level.

• 4-digit NACE 46.52 = "Wholesale of electronic and telecommunications equipment" — specific, targeted
• 2-digit NACE 46 = "Wholesale trade" — includes wholesalers of food, textiles, machinery, etc.

Using 4-digit codes yields a homogeneous set performing very similar functions. Broader 2-digit codes introduce "false positives"—companies in the same sector but with different economics.

Primary vs. Secondary Codes

Companies have one primary code (main business) and may have secondary codes (other activities). Filter by primary code initially—this ensures the company's core business matches your tested party. If results are too narrow, you can include secondary codes, but expect more manual vetting.

Code Accuracy Caveat

Industry codes aren't perfectly precise. They're often self-reported or assigned by analysts and may be outdated. A company's registered code doesn't always reflect its actual current business. The EU JTPF notes industry classification inconsistencies and recommends combining codes with other elements.

Best practice: Codes are a starting point; validate with keywords and qualitative review of business descriptions. Don't rely on code granularity alone.

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Geographic Filters

Geographic filters limit the search to companies operating in relevant markets. Market differences—competition, cost levels, consumer behavior—can significantly affect profitability.

Local vs. Regional Approach

Start with the tested party's country. Many jurisdictions prefer local comparables where available, though explicit legal requirements vary:

If a single-country search yields insufficient comparables (practitioners often consider fewer than 5-7 a concern), expand to the region:

• DACH region (Germany, Austria, Switzerland) for German tested parties
• Nordic countries for Scandinavian transactions
• Pan-European when single-country data is insufficient

Documenting Geographic Rationale

Whatever scope you choose, document why:

"A local search in [Country X] yielded only 3 independent companies after all filters. Therefore, the search was broadened to include [Country Y and Z], which share similar economic conditions and wage structures with [Country X]."

For detailed guidance, see our Regional vs Local Comparables Guide.

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Independence Indicators

Independence filters exclude companies that are part of multinational groups. This is critical—comparables analysis relies on observing uncontrolled results. A subsidiary's financials might be influenced by transfer pricing, making them unsuitable comparables.

BvD Independence Indicator

Bureau van Dijk databases (Orbis, Amadeus) use a standardized indicator:

Standard Practice: Include A and B, Exclude C and D

The 50% threshold balances data availability with comparability. Applying stricter independence criteria (e.g., requiring no shareholder >25%) can materially reduce the available sample—sometimes to the point where a meaningful analysis becomes difficult.

Handling Unknown (U) Companies

Companies with "U" status have no ownership information available. A common conservative approach is to exclude them by default—they could be undisclosed subsidiaries. If your sample is critically small, you might include U-rated companies but investigate each manually. Document your reasoning.

Bottom line: Independence is a core screening criterion for external company comparables. In BvD terms, analysts typically include A/B and exclude C/D, subject to local rules and the specific facts of the analysis.

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Size and Revenue Filters

Size filters exclude companies that are extremely larger or smaller than the tested entity. Scale differences affect profitability—larger companies may have economies of scale; very small companies may be volatile or unsustainable.

Common Thresholds

These ranges are frequently used in practice; adjust based on tested party characteristics and local norms:

Setting Thresholds Relative to Tested Party

The tested party's size should guide your thresholds. For a €25M distributor:

• Minimum: €5M (excludes one-person operations)
• Maximum: €100-250M (excludes multinational distributors with different scale)

If the tested party is €500M, you might set minimum €50M and no maximum.

Watch the Sample Count

Size filters significantly reduce candidate numbers. If moving from €1M to €5M minimum drops your pool from 200 to 30, that's fine. If it drops from 30 to 3, reconsider.

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Financial Data Availability Filters

These filters ensure companies have sufficient data for analysis. There's no point including a company if it lacks the financial metrics you need.

Years of Data

Common practice: Require at least 3 years of financial data where available. Transfer pricing analysis often uses multi-year data to understand business cycles and improve comparability—though this is context-dependent, not a universal minimum. Hungary's new TP decree framework (2025) specifies that search strategies should reference the three years preceding the tested year (confirm current effective date and phased applicability).

Required Fields

For TNMM/CPM analysis, you need:

• Revenue (operating revenue/sales)
• Operating profit (EBIT or similar)
• For ROA: Total assets
• For WCA: Accounts receivable, payable, inventory

Set database criteria to exclude companies with null values in critical fields for your analysis years.

Consolidated vs. Unconsolidated

For comparables, prefer unconsolidated (standalone) financials where available to reflect entity-level performance. If a database includes both, filter to unconsolidated. Use consolidated only with strong justification (e.g., when unconsolidated data is unavailable or the consolidated entity closely mirrors the tested party's profile).

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Profitability and Loss Exclusion Filters

Profitability filters screen out companies with problematic financial patterns. However, they're contentious—excluding all loss-makers can introduce upward bias.

Common Approaches

Practical Guidelines

These are policy choices, not universal rules—adjust based on case-specific analysis consistent with OECD guidance:

• Exclude companies with losses in all analysis years (likely non-viable or facing extraordinary circumstances)
• Allow companies with 1 loss year out of 3 (may reflect normal business cycles)
• Consider excluding companies with negative equity (signal of financial distress)

Link to Loss-Making Comparables

For detailed guidance on handling loss-makers, see our Loss-Making Comparables Guide.

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Recommended Filter Sequence

Apply filters in a logical order—from broad to narrow. This commonly efficient sequence maximizes efficiency and ensures the most relevant cuts happen first. Other sequences can be equally defensible if documented and consistently applied.

Commonly Used Sequence

1. INDUSTRY (NACE/SIC code)
2. GEOGRAPHY (country/region)
3. INDEPENDENCE (exclude subsidiaries)
4. STATUS (active only, exclude start-ups)
5. DATA AVAILABILITY (≥3 years)
6. SIZE (revenue range)
7. PROFITABILITY (loss filter)
8. MANUAL SCREENING

Why This Order Works

1. Industry first — Establishes the fundamental universe of comparable businesses
2. Geography second — Narrows to relevant markets
3. Independence early — Removes thousands of subsidiaries before detailed filtering
4. Status/Data before size — Ensures companies are usable before evaluating metrics
5. Size before profitability — Fundamental comparability factor comes before outcome-based filters
6. Profitability last — Most contentious filter; applied when you can assess impact on sample

This sequence ensures that when you apply profitability filters, you're evaluating companies of similar scale in the same industry—not mixing apples and oranges.

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The Accept/Reject Matrix (Your Audit Trail)

An accept/reject matrix is the simplest way to make your screening defensible. It shows that companies were included or excluded based on predefined criteria, not outcome-driven judgment.

Minimum columns (recommended)

What makes it audit-ready

• Specific reasons (avoid “not comparable” as a standalone reason)
• Consistency (if one company is rejected for a threshold, others failing that threshold should be rejected too)
• Traceability (link each reason to your criteria and the evidence source)

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Common Screening Mistakes

Mistake 1: Over-Filtering

Symptom: Zero or near-zero companies remain after screening.

Cause: Too many restrictive criteria stacked together—narrow industry code AND single country AND strict size AND no losses ever.

Fix: Relax filters progressively. Check which filter eliminated the most companies and consider whether it's too strict.

Mistake 2: Under-Filtering

Symptom: Hundreds or thousands of companies remain, requiring impossible manual review.

Cause: Broad 2-digit industry code, no size filter, no independence filter.

Fix: Apply sensible thresholds. Add revenue minimum, ensure independence is checked.

Mistake 3: Wrong or Outdated Industry Code

Symptom: Companies in the set don't actually do what the tested party does.

Cause: Relying on codes that don't match actual activities, or codes that were accurate years ago but not now.

Fix: Verify code by reading descriptions of companies in that category. Consider multiple codes if the tested party spans activities.

Mistake 4: Ignoring Independence

Symptom: Final comparables include subsidiaries of large groups.

Cause: Database didn't have independence filter applied, or filter was set incorrectly.

Fix: Always apply independence filter. For public companies, manually check for parent ownership.

Mistake 5: Arbitrary Thresholds Without Justification

Symptom: Revenue cutoff of exactly €7.3M with no explanation.

Cause: Setting thresholds to achieve a desired outcome rather than based on comparability rationale.

Fix: Every threshold should be tied to tested party characteristics or standard practice. Document reasoning.

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Practical Example: Screening a German Distributor

This illustrative example shows how filters progressively narrow the candidate pool. Actual counts vary by database version, year, and specific filter settings.

Tested Party: German limited-risk distributor of electronic components, €25M annual revenue.

Database: Orbis/Amadeus

Step-by-Step Search (Illustrative)

Threshold Rationale + Sensitivity (How to Defend Your Cutoffs)

Thresholds should be set to improve comparability, not to force an outcome. The easiest way to defend them is to document:

1. Why the filter matters economically (e.g., scale differences affect margins; subsidiaries may embed transfer pricing)
2. Why the threshold fits the tested party (anchored to tested party size/market)
3. What it did to the candidate pool (counts before/after each step)

Using the illustrative funnel above:

• After the data availability step, the pool was ~200.
• Applying the revenue threshold (€5M–€100M) reduced it to ~80.

That reduction is normal. But if a single threshold wipes out almost everything, you should reconsider whether it’s too strict (or whether an earlier filter was too narrow). Your audit defense is the narrative: “we set thresholds to align scale and ensure data completeness, and we monitored the impact at each step.”

Documentation Summary

"The comparables search was conducted using Orbis (accessed December 2025). Companies were selected based on NACE Rev.2 code 46.52 (wholesale of electronic equipment), limited to DACH region to ensure market comparability. Only independent companies (BvD Independence A or B) with at least 3 years of financial data were included. Revenue thresholds of €5M-€100M were applied to align with the tested party's scale (€25M). Persistent loss-makers (losses in all three years) were excluded. The quantitative screening yielded 60 companies for manual review."

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Related Topics

Benchmarking Guides:

• Complete Benchmarking Study Guide — Full methodology overview
• Regional vs Local Comparables — When to expand geography
• Loss-Making Comparables — Handling companies with losses
• Common Benchmarking Mistakes — Pitfalls to avoid
• NACE Code Selector Tool — Find the right industry code

Documentation Guides:

• Transfer Pricing Documentation Guide — Complete documentation hub
• Local File Best Practices — Documenting screening methodology
• Common Documentation Weaknesses — Avoiding documentation pitfalls

Glossary:

• Quantitative Screening — Definition and filter sequence
• Independence Indicator — BvD codes explained
• NACE Code — EU industry classification
• SIC Code — US industry classification
• Manual Screening — Next step after quantitative filters

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Frequently Asked Questions

In what order should I apply screening filters?

Apply filters from broad to narrow: industry → geography → independence → status → data availability → size → profitability. This sequence ensures maximum efficiency—you eliminate large swaths of irrelevant companies early (different industries, subsidiaries) before fine-tuning on size and profitability. Independence and data availability are objective, easy-to-apply filters that should come before more subjective decisions.

How many comparables should remain after quantitative screening?

As a rule of thumb, many practitioners aim for a few dozen companies for manual review—enough to yield roughly 5-15 final comparables after qualitative screening. The exact number depends on the transaction and available data. If you have very few (e.g., fewer than 10) after quantitative filters, consider broadening geography or industry code. If you have more than 200, tighten size thresholds or add stricter criteria—manual review of hundreds is impractical and suggests under-filtering.

What's the difference between BvD Independence indicators A, B, C, and D?

A = No shareholder owns >25% (truly independent). B = No shareholder >50%, but one owns 25-50% (minority-held, still acceptable). C = Majority owned by a shareholder >50% (subsidiary—exclude). D = Direct subsidiary with >50% direct ownership (exclude). Standard practice: include A and B, exclude C and D. The U (Unknown) category requires case-by-case judgment.

Should I use NACE, SIC, or NAICS codes?

Use the system native to your database and jurisdiction. For European searches (Amadeus, Orbis Europe), NACE Rev.2 is standard. For U.S. searches (Compustat), SIC or NAICS work well—NAICS is more modern. Most global databases support multiple systems and can cross-reference. The key is using 4-digit specificity in whatever system you choose.

How do I set appropriate revenue thresholds?

Base thresholds on the tested party's size. A common approach: set minimum at roughly 1/5 of tested party revenue (to exclude micro-firms) and maximum at 5-10x (to exclude very large companies). For a €25M tested party, €5M-€100M is reasonable. Always document rationale: "Revenue threshold set to exclude firms materially smaller or larger than tested party to ensure comparability in operations scale."

What if my search yields too few comparables?

First, verify your filters aren't too restrictive—check if any single filter caused a dramatic drop. Then consider: (1) expanding geography to a broader region, (2) including a related industry code, (3) lowering minimum revenue, (4) accepting companies with one loss year instead of zero. Document any broadening: "Local search yielded insufficient comparables; expanded to pan-European as permitted under OECD guidelines when local data is sparse."

Should I exclude all loss-making companies?

No. Automatically excluding all loss-makers is too aggressive and can bias results upward. ¶3.64 explicitly states companies shouldn't be rejected solely for having losses. Exclude only persistent loss-makers (losses in all years of the analysis period) and investigate single-year losses to determine if they reflect normal business cycles or extraordinary circumstances. Document your analysis for each loss-maker considered.

How do I document my screening criteria?

Create a search strategy report listing: (1) database used and search date, (2) each filter criterion with threshold, (3) rationale for each threshold, (4) number of companies remaining after each filter, and (5) final count proceeding to manual review. This documentation should allow someone to replicate your search. Many practitioners include this as a dedicated section in the benchmarking study or as an appendix.

Related OECD Guidance

The OECD Transfer Pricing Guidelines provide guidance on comparable selection and screening:

• Chapter III: Comparability Analysis – Primary guidance on comparable selection process and comparability factors
• ¶3.64 – Guidance on loss-making comparables: companies shouldn't be rejected solely for having losses
• ¶3.43-3.46 – Guidance on the search process and identifying potential comparables
• ¶3.2 – The five comparability factors that should guide screening criteria