CET vs CGBD

11017 vs Carlyle Secured Lending, Inc. — Valuation Comparison 2026

CET

Asset Management
11017
Quality
2.0
out of 10
Value Trap
Price
$53.10
Last close
Models
13/13
Active
VS

CGBD

Asset Management
Carlyle Secured Lending, Inc.
Quality
6.6
out of 10
Value Trap
Price
$10.92
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType CET Fair ValueCET Upside CGBD Fair ValueCGBD Upside
Bayesian DCF Intrinsic $14.06 -73.5%
Earnings Power Value Intrinsic $21.14 -60.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $295.84 +457.1% $15.29 +40.1%
ML-RIV Intrinsic $52.66 -0.8% $22.95 +110.2%
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 CET vs CGBD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CET vs CGBD — Which Stock Is More Undervalued?

CGBD scores higher with a 6.6/10 quality rating vs CET's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing 11017 (CET) and Carlyle Secured Lending, Inc. (CGBD) 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.

CET currently trades at $53.10 with a QOC of 2.0/10, while CGBD trades at $10.92 with a QOC of 6.6/10.

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).