How to compare deal terms across CLO managers

TL;DR: Comparing deal terms across CLO managers means normalising each manager's indenture language into the same set of datapoints and then benchmarking — because managers draft the same economic concept in different words. A plain text search won't line them up; you need the underlying terms extracted consistently, so you can put manager against manager and manager against market on a like-for-like basis.

Why comparing across managers is hard

Two CLO indentures can implement the same idea — say, the flexibility to buy discounted obligations, or the CCC bucket haircut — with entirely different defined terms, cross-references and drafting. That's the core difficulty: the concept is comparable but the language isn't. So the moment you try to compare "reinvestment flexibility" or "manager discretion" across managers, you hit three problems: the terms are named differently, they're located differently in each document, and they interact with other defined terms differently. String-matching or reading deal by deal either misses the match or takes days.

How to compare terms across managers

1. Choose the terms that differentiate managers. The ones that actually vary and matter — reinvestment criteria and flexibility, CCC and concentration limits, LMO baskets, OC/IC cushions, WAL covenant, and eligibility carve-outs.
2. Extract them normalised. Capture each term as the same datapoint across every deal, regardless of how each manager drafts it, so like sits against like.
3. Compare manager vs manager and vs market. Put each manager's distribution next to the others and against the market to see who runs manager-friendly and who runs investor-friendly.
4. Blackline to see the drafting. Where a difference matters, compare the actual language to understand how a manager achieves the flexibility, not just that they do.

Semeris is built for exactly this: it extracts normalised, analyst-verified datapoints across 2,000+ US and 893 EU CLOs, so any term can be compared across managers with Market Compare, and any two deals blacklined in seconds. The DvES adds a ready-made lens — scoring each indenture's debt-vs-equity orientation across 3,300+ structures (~90% of the US and EU BSL market) so managers can be ranked on how their documentation leans.

Why generic AI gets this wrong

The most common failure mode is surface-term matching. A general model compares the words managers use rather than the concepts they encode, so it either reports two managers as different when they're economically identical, or as identical when the drafting hides a real difference. It has no normalisation layer and no verified cross-deal dataset to benchmark against. Semeris compares the underlying, analyst-verified terms — not the surface language — so the comparison reflects the economics.

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Related questions

• Why is comparing terms across CLO managers hard?
• How do I benchmark a single manager against the market?
• How do I quantify deviation from market-standard CLO terms?

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