From CLO tranche to borrower: A Connected View of CLO Exposure

See how Bloomberg connects normalized CLO and loan-level data to provide a clearer, more traceable view of portfolio exposure.

Connect the security, its collateral and the corporate borrower.

As the CLO market has grown in size, complexity and global reach, supporting data infrastructure hasn’t kept pace leading to  market participants having to piece together information from multiple sources—often with different identifiers and different ways of describing the same structural features.

This fragmentation can make it difficult to maintain a consistent view of a CLO security, the loans held within its portfolio and the corporate borrowers ultimately driving exposure.

Join Bloomberg for a practical demonstration of how its connected CLO data brings these layers together. By combining CLO Reference Data, CLO Loan Level Details, Syndicated Loan Reference Data and Corporate Structure Data, investors can move efficiently from CLO tranches to underlying loan exposure and associated corporate entity.

What you will learn
See how Bloomberg’s interoperable datasets can help you:

• Compare portfolios by CLO security, manager, rating and overlap
• Move from the CLO tranche and deal level to the loans held within each CLO portfolio
• Link underlying loans to associated corporate entities and corporate structures
• Identify concentrations and overlaps that are not visible in any single dataset
• Create a more consistent and scalable view of CLO exposure across enterprise workflows

By normalizing reported holdings and mapping identifiers, Bloomberg makes the data easier to link, analyze and scale across investment and risk workflows.

Bloomberg’s CLO data integrates with its broader Enterprise Data offerings through a unified data model, helping clients combine CLO and loan-level information with complementary reference, liquidity, regulatory and credit-risk data.

Who should attend
This webinar is designed for CLO and structured-credit portfolio managers, investment analysts, risk professionals, quantitative researchers, security master teams and enterprise data professionals working with funds, CLOs or syndicated loans.