PCM vs PDI

PCM Fund, Inc. vs PIMCO Dynamic Income Fund — Valuation Comparison 2026

PCM

Asset Management
PCM Fund, Inc.
Quality
1.7
out of 10
Value Trap
Price
$5.66
Last close
Models
10/13
Active
VS

PDI

Asset Management
PIMCO Dynamic Income Fund
Quality
1.7
out of 10
Value Trap
Price
$16.74
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType PCM Fair ValuePCM Upside PDI Fair ValuePDI Upside
Bayesian DCF Intrinsic $1.50 -73.5% $4.94 -70.5%
Earnings Power Value Intrinsic $7.59 -56.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 $7.66 +35.3%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PCM vs PDI — Which Stock Is More Undervalued?

Both PCM and PDI score 1.7/10 on quality. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing PCM Fund, Inc. (PCM) and PIMCO Dynamic Income Fund (PDI) 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.

PCM currently trades at $5.66 with a QOC of 1.7/10, while PDI trades at $16.74 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).