PSLV vs RCS

"Sprott Physical Silver Trust" vs PIMCO Strategic Income Fund, In — Valuation Comparison 2026

PSLV

Asset Management
"Sprott Physical Silver Trust"
Quality
6.5
out of 10
Value Trap
22
SAFE
Price
$24.12
Last close
Models
7/13
Active
VS

RCS

Asset Management
PIMCO Strategic Income Fund, In
Quality
1.9
out of 10
Value Trap
Price
$5.37
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType PSLV Fair ValuePSLV Upside RCS Fair ValueRCS Upside
Bayesian DCF Intrinsic $3.78 -84.3% $1.42 -73.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $9.76 +81.7%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $11.89 -50.7% $2.37 -56.1%
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 PSLV vs RCS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PSLV vs RCS — Which Stock Is More Undervalued?

PSLV scores higher with a 6.5/10 quality rating vs RCS's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing "Sprott Physical Silver Trust" (PSLV) and PIMCO Strategic Income Fund, In (RCS) 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.

PSLV currently trades at $24.12 with a QOC of 6.5/10, while RCS trades at $5.37 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).