Structured Finance Journal

This paper lays out a transparent data structure designed to produce a clean dataset that serves as a benchmark for the Agency Single-Family Collateralized Mortgage Obligations (CMO) market. We establish the integrity of the new dataset through the application of simple accounting identity, which is both internally consistent and transparent. The paper concludes with two applications of the dataset. First, it examines the relationship between CMO lockup ratios and the market shares of Agency MBS production. Second, it discusses mortgage market and security liquidity in the context of float rates at the Agency, tranche and security levels.

Asset-based finance (ABF) is reshaping corporate funding models. By monetizing receivables, loans, leases, and other operating assets through institutional and private channels, ABF creates flexible access to capital — from bilateral facilities to full-scale securitizations. The result: lower funding costs, broader liquidity, and stronger balance-sheet alignment. Designed for CFOs and boards, this paper provides a practical roadmap for turning assets into strategic funding tools, outlining key concepts, execution pathways, and readiness imperatives for long-term success.

We’re excited to share the publication of The Power of Universal LLM Data Ingestion to Build Generative AI-Powered Metadata, our debut SFJournal submission. This paper explores how Large Language Models (LLMs) can support more efficient metadata creation — enhancing efficiency, accuracy, and scalability in structured finance and private credit— and outlines challenges and considerations for implementation.