用估值数据定义公司相似性，提升私市公司估值参考效率。
What Makes a Peer? Valuation-Anchored Similarity in Private Markets
- 基于估值而非静态特征构建相似性，融合梯度提升树与叶节点共现
- 在27万家企业中验证，对齐估值的相似性比传统方法更准
- 适合做尽调、组合管理的投资者，结果可解释性强
随着更多投资者进入私市，受限于透明度低、披露少、交易稀疏，识别有经济意义的同行公司成为估值、尽调、组合构建和风险管理的核心挑战。本文提出一种基于集成树的监督式相似性学习框架，通过市场估值视角定义公司相似性，而非依赖静态特征匹配或语义描述。具体地，利用CatBoost梯度提升决策树模型在可观测私企估值上训练，并从集成模型中重要性加权的叶节点共现中提取估值感知的相似性度量。该度量捕捉共享估值驱动因素，同时适应私市常见的非线性关系、混合数据类型和大量缺失数据。基于包含约27万家企业、超5.3万家具有可观察或推导出的投后估值的全球私市企业库（覆盖多行业、多地理区域及不同融资阶段），实证显示该框架在下游k近邻估值任务中优于传统距离度量与文本嵌入方法，且保持案例可解释性。
原文摘要 · Abstract (English)
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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