SAMG vs SATA

Silvercrest Asset Management Gr vs Strive, Inc. - Variable Rate Se — Valuation Comparison 2026

SAMG

Asset Management
Silvercrest Asset Management Gr
Quality
6.1
out of 10
Value Trap
25
LOW
Price
$11.17
Last close
Models
12/13
Active
VS

SATA

Asset Management
Strive, Inc. - Variable Rate Se
Quality
5.2
out of 10
Value Trap
27
LOW
Price
$100.00
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType SAMG Fair ValueSAMG Upside SATA Fair ValueSATA Upside
Bayesian DCF Intrinsic $16.34 +46.3%
Earnings Power Value Intrinsic $4.00 -64.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $60.60 -39.3%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $1.13 -91.4% $7.24 -92.8%
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 SAMG vs SATA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SAMG vs SATA — Which Stock Is More Undervalued?

SAMG scores higher with a 6.1/10 quality rating vs SATA's 5.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Silvercrest Asset Management Gr (SAMG) and Strive, Inc. - Variable Rate Se (SATA) 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.

SAMG currently trades at $11.17 with a QOC of 6.1/10, while SATA trades at $100.00 with a QOC of 5.2/10.

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).