VENIONAIRE DEALMATRIX MULTIPLES
Venionaire DealMatrix Multiples Methodology
Explore private market valuation multiples across 140+ sectors with benchmarks structured by stage and region. Updated quarterly. Built for practitioners.
Why classical multiple approaches fall short
Traditional comparable company analysis relies on a limited set of reference companies and fails to account for macroeconomic influences. Valuation multiples are not arbitrary market conventions — they are condensed representations of future cash flows, growth expectations, and discount rates.
When interest rates rise, the discount rate embedded in every multiple rises with it, compressing valuations across the board. When the growth outlook weakens, multiples fall even if revenues hold steady. Static, time-invariant multiples are explicitly discouraged by IPEV Guidelines 2025.
Model Architecture
DealMatrix multiples are derived through a six-step econometric model combining 25 years of public equity data, macroeconomic indicators and regional + stage-based calibration — producing IPEV 2025 compliant valuation benchmarks across 140+ sectors, 6 regions, and 7 funding stages.
Data Acquisition
The data extraction layer draws upon a curated universe of approximately 200 publicly traded equity indices, sourced from public equity markets and annual financial statements of selected public companies. Each index is classified under the Global Industry Classification Standard (GICS) and assigned to one of six geographic regions. These public market comparables serve as the empirical baseline from which private market benchmarks are derived.
Statistical Cleaning
This stage processes the raw extraction dataset by removing unusable records, aggregating multiple index observations at subsector level via simple averaging, and correcting extreme values that would otherwise distort valuation benchmarks. Outlier bounds are set at 300× for EV/EBITDA and 60× for EV/Sales. The result is a complete and normalised quarterly multiples panel that preserves broad market coverage while improving statistical reliability.
Econometric Modelling & Averaging
The cleaned multiples panel is combined with eight quarterly macroeconomic indicators per region — including GDP, interest rates, inflation, and money supply. A mixed linear model separates macro, sector, time, and region effects, yielding a reported multiple (y), a model-predicted fair value (ŷ), and a conservative lower bound. These three outputs are averaged into a single stable Venionaire Multiple, dampening market overreactions in both directions.
Regional Adjustment
A regional coefficient α is applied to each multiple, normalised to North America as the reference market. Coefficients are derived from country-level enterprise value medians aggregated to the regional level — reflecting structural differences in market depth, investor appetite, and valuation culture across Europe, Asia-Pacific, Australia, South America, and Africa.
Industry Weighting
Because startups rarely map to a single GICS sub-industry, each of approximately 150 DealMatrix categories is associated with a weighted blend of public sub-industries whose listed companies are most economically comparable. The weights sum to 100% and are maintained by the DealMatrix team, reflecting the revenue model, cost structure, and growth profile of each startup category.
Stage Adjustment
A stage factor β is applied from Pre-Seed through Series E, derived analytically from a growth-adjusted present value framework. Early-stage companies command multiples above public comparables due to growth expectations that outweigh the higher required return. At Series E, decelerating growth and a narrowing WACC spread produce a pre-IPO discount, with β falling below 1.
From GICS sub-industries to DealMatrix categories
GICS was designed for established listed companies — there is no GICS category for SaaS, no classification for marketplace platforms, no sub-industry for AI infrastructure. Each DealMatrix category is therefore mapped to a weighted combination of the GICS sub-industries whose listed companies are most economically comparable.
For example: Artificial Intelligence draws from IT Consulting, Data Processing, Systems Software, and Application Software. SaaS draws from Application Software, Systems Software, and Data Processing. The weights sum to 1 and are maintained by the DealMatrix team, reflecting how closely each public sub-sector matches the revenue model and cost structure of startups in that category.
Scope of the model
Each sector-stage-region combination is modelled independently — not derived from a single global average. The terminal dataset contains approximately 49,000 rows updated quarterly.
The DealMatrix methodology follows the International Private Equity and Venture Capital Valuation Guidelines 2025 — effective for reporting periods beginning on or after 1 April 2026 — the global best-practice standard for fair value measurement in private capital funds. The 2025 update reinforces the calibration principle: valuation assumptions must evolve at each measurement date in response to movements in the public comparable basket. Static, time-invariant multiples are explicitly discouraged.