什么构成了同行？私人市场中基于估值锚定的相似性
What Makes a Peer? Valuation-Anchored Similarity in Private Markets
- BlackRock, Inc.（贝莱德集团）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对私人市场可比公司识别难题，提出基于CatBoost的集成树监督相似性学习框架，在下游k近邻估值任务中优于传统方法，保留可解释性。
AI中文摘要：
随着越来越多的投资者考虑投资私人市场，并面临透明度有限、披露信息稀疏和交易不频繁的问题，为估值、尽职调查、投资组合构建和风险管理识别具有经济意义的可比公司是一项基本挑战。我们提出了一种基于集成树的监督相似性学习框架，该框架通过市场估值而非静态特征匹配或语义描述来定义公司相似性。具体而言，我们在观察到的私人公司估值上训练了CatBoost梯度提升决策树模型，并从集成中重要性加权的叶节点共现中推导出一种感知估值的相似性度量。该相似性度量捕捉了共享的估值驱动因素，同时适应了私人市场中常见的非线性关系、混合数据类型和普遍存在的缺失数据。使用约27万家公司的全球私人市场整体，其中包括超过5.3万家具有观察到或可推导的投资后估值的公司，涵盖多个行业、地区和交易阶段，我们证明所提出的相似性框架在评估的行业组中，在下游k近邻估值任务中优于传统的基于距离和基于文本嵌入的方法，同时保留了基于案例的可解释性。
英文摘要：
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.
