A Machine Learning–Based Public Market Equivalent Framework for Estimating Default Risk in Private Credit

Abstract

In this article, the authors introduce a public market equivalent framework to estimate default risk for private firms when financial statement fundamentals are unavailable or stale. The approach learns a supervised similarity metric from publicly traded corporate bond (from both public and private issuers) characteristics and risk measures using a random forest model and then uses random forest geometry- and accuracy-preserving (RF-GAP) proximities to identify a small, sector-consistent set of comparable public issuers for each private issuer. Comparable issuers’ ratings are aggregated into a similarity-weighted implied rating for the private issuer, which is subsequently mapped to a rating-implied probability of default using transition and default statistics. The framework is designed to balance predictive accuracy with interpretability by providing a transparent, peer-based explanation of each private issuer’s inferred credit quality.