How much do BDC portfolios overlap, and where do they disagree on the marks?

For every pair of BDCs, what fraction of portfolio is the same borrowers? And for every borrower held by multiple BDCs, how widely do those BDCs disagree on what the credit is worth? This module answers both questions in a single analytical surface. The cross platform overlaps reveal systemic concentration risk. The mark dispersion finds the credits where someone's valuation is wrong.

Methodology: Raymond James pairwise overlap formula · Borrower names normalized across 614 raw labels into canonical entities · Equity holdings excluded per institutional convention · Loading coverage...
Analysis modules: 01 · Universe time series 02 · Borrower overlap 03 · Manager cohorts 04 · Dividend coverage 05 · Non traded liquidity 06 · Sector concentration
Layer 01

The overlap picture

How much portfolio overlap exists across the universe · and which BDC pairs are the most exposed to the same borrowers.
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Unique borrowers
after name normalization
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Held by 2+ BDCs
the cross holding universe
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Held by 5+ BDCs
widely syndicated names
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Held by 10+ BDCs
universally syndicated

Top cross-platform overlapping pairs (intra-platform pairs excluded · they are structural)

Computing pairwise overlaps...
Layer 02

Pairwise overlap matrix

The top BDCs by NAV scored against each other. Each cell is the percentage of portfolio shared between the two funds.
Show
Showing - BDCs
Building overlap matrix...
Overlap (% of smaller portfolio shared): 0%
50%+ Cross platform pair
Layer 03

Mark dispersion · where the BDCs disagree

For every borrower held by 2+ BDCs that report marks, the spread between the highest and lowest valuation. Wide spreads suggest one BDC is wrong.
Sort
Holders
Showing - borrowers
Borrower
# BDCs
Min
Median
Max
StD
Marks distribution
Computing mark dispersion across the universe...

How overlap is computed

Per the Raymond James and PIMCO convention, pairwise overlap between two BDCs is the sum of min(weight_A, weight_B) across every shared borrower, where weight is the holding's fair value as a percent of the BDC's portfolio. The result is a number from 0% (no shared borrowers) to 100% (identical portfolios).

Borrower names are normalized across roughly 25,000 raw labels using lowercase, punctuation stripping, and trailing corporate suffix removal (Inc, LLC, Corp, Holdings, LP, etc). About 24,700 unique normalized entities result. Schedule of Investment section headings ("Non-Controlled Non-Affiliated Portfolio Investments") are filtered out as they're regulatory grouping labels, not real borrowers.

Equity holdings (warrants, preferred stock, common stock) are excluded from the overlap computation per Raymond James convention, because equity valuations behave differently from debt valuations. Debt instruments only · 1L, 2L, mezzanine, unsecured.

What mark dispersion tells you

When two BDCs hold the same debt of the same borrower and mark it at materially different values, one of them is wrong. The most likely error is at the lender that took its position via participation or syndication, since the lead arranger has better real time information on borrower performance.

The famous case is CION marking Isagenix at 98 while peers marked at 86 to 88 · a marker spread of roughly 10 points that CreditSights flagged six months before Isagenix went into restructuring. Once the company defaulted, the marks converged downward to roughly 60.

Marks outside the 30 to 130 range are excluded as data quality outliers. The dispersion metric is the population standard deviation across all BDCs reporting a mark for that borrower. We use the largest fair value position when the same BDC reports multiple holdings for the same borrower (e.g. a tranche split).