BGB vs BHK

Blackstone / GSO Strategic Cred vs Blackrock Core Bond Trust Black — Valuation Comparison 2026

BGB

Asset Management
Blackstone / GSO Strategic Cred
Quality
1.7
out of 10
Value Trap
Price
$11.36
Last close
Models
6/13
Active
VS

BHK

Asset Management
Blackrock Core Bond Trust Black
Quality
1.9
out of 10
Value Trap
Price
$9.04
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType BGB Fair ValueBGB Upside BHK Fair ValueBHK Upside
Bayesian DCF Intrinsic $3.01 -73.5% $2.39 -73.5%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $8.48 -24.9% $7.87 -13.0%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for BGB vs BHK — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BGB vs BHK — Which Stock Is More Undervalued?

BHK scores higher with a 1.9/10 quality rating vs BGB's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Blackstone / GSO Strategic Cred (BGB) and Blackrock Core Bond Trust Black (BHK) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

BGB currently trades at $11.36 with a QOC of 1.7/10, while BHK trades at $9.04 with a QOC of 1.9/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).