SPPP vs STK

"Sprott Physical Platinum and P vs Columbia Seligman Premium Techn — Valuation Comparison 2026

SPPP

Asset Management
"Sprott Physical Platinum and P
Quality
6.3
out of 10
Value Trap
18
SAFE
Price
$14.91
Last close
Models
11/13
Active
VS

STK

Asset Management
Columbia Seligman Premium Techn
Quality
1.7
out of 10
Value Trap
Price
$55.60
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType SPPP Fair ValueSPPP Upside STK Fair ValueSTK Upside
Bayesian DCF Intrinsic $1.80 -87.9% $14.72 -73.5%
Earnings Power Value Intrinsic $70.78 +374.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $16.17 -68.3%
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 SPPP vs STK — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SPPP vs STK — Which Stock Is More Undervalued?

SPPP scores higher with a 6.3/10 quality rating vs STK's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing "Sprott Physical Platinum and P (SPPP) and Columbia Seligman Premium Techn (STK) 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.

SPPP currently trades at $14.91 with a QOC of 6.3/10, while STK trades at $55.60 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).