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) characteris...
R. Yadav, Anubhab Saha, Saurabh Singh et al.· The Journal of Private Marke...· 0 citations
An ensemble tree-based supervised similarity learning framework is proposed that defines company similarity through the lens of market valuation rather than static feature matching or semantic descriptions, and improves upon traditional distance-based and text-embedding-based approaches in downstream k-nearest-neighbor...
Sebastian Frank, Jingrao Lyu, Max Jarmey et al.· 0 citations
Across financial filings and earnings-call transcripts, it is found that LLM-based compression can produce fluent and factually plausible compressed contexts that nevertheless alter downstream decisions, suggesting that financial compression should be evaluated not only by efficiency or factuality, but also by its abil...
Hoyoung Lee, Suhwan Park, Seung-Han Lee et al.· arXiv.org· 3 citations
This paper presents the first systematic empirical study in the financial domain to characterize how an adversarial signal enters a multi-agent trading system and how far it survives toward the decision, and evaluates four communication topologies under data- and agent-level attacks.
CheolWon Na, Hao Ni, Lukasz Szpruch et al.· 0 citations
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