CXH vs DBL

MFS Investment Grade Municipal vs DoubleLine Opportunistic Credit — Valuation Comparison 2026

CXH

Asset Management
MFS Investment Grade Municipal
Quality
1.7
out of 10
Value Trap
Price
$7.61
Last close
Models
11/13
Active
VS

DBL

Asset Management
DoubleLine Opportunistic Credit
Quality
1.7
out of 10
Value Trap
Price
$14.37
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType CXH Fair ValueCXH Upside DBL Fair ValueDBL Upside
Bayesian DCF Intrinsic $2.01 -73.5% $3.80 -73.5%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $3.53 -53.6% $11.54 -19.7%
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 CXH vs DBL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CXH vs DBL — Which Stock Is More Undervalued?

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

Comparing MFS Investment Grade Municipal (CXH) and DoubleLine Opportunistic Credit (DBL) 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.

CXH currently trades at $7.61 with a QOC of 1.7/10, while DBL trades at $14.37 with a QOC of 1.7/10.

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).