What Makes a Peer? Valuation-Anchored Similarity in Private Markets

• URL: http://arxiv.org/abs/2608.12594v1
• Date: Wed, 12 Aug 2026 21:06:51 GMT
• Title: What Makes a Peer? Valuation-Anchored Similarity in Private Markets
• Abstract summary: We propose an ensemble tree-based supervised similarity learning framework that defines company similarity through the lens of market valuation.<n>We derive a valuation-aware similarity metric from importance-weighted leaf-node co-occurrences across the ensemble.<n>Using a global private-market universe of approximately 270,000 companies, we demonstrate that the proposed similarity framework improves upon traditional distance-based approaches.
• Score: 0.5493496184627729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying economically meaningful peer companies for comparison is a fundamental challenge for valuation, due diligence, portfolio construction, and risk management. We propose an ensemble tree-based supervised similarity learning framework that defines company similarity through the lens of market valuation rather than static feature matching or semantic descriptions. Specifically, we train a CatBoost gradient-boosted decision tree model on observed private company valuations and derive a valuation-aware similarity metric from importance-weighted leaf-node co-occurrences across the ensemble. The similarity metric captures shared valuation drivers while accommodating nonlinear relationships, mixed data types, and pervasive missing data common in private markets. Using a global private-market universe of approximately 270,000 companies, including more than 53,000 firms with observed or derivable post-money valuations spanning multiple industries, geographies, and deal stages, we demonstrate that the proposed similarity framework improves upon traditional distance-based and text-embedding-based approaches in downstream k-nearest-neighbor valuation tasks in the evaluated industry groups, while retaining case-based explainability.

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