NSTS vs NWG

NSTS Bancorp, Inc. vs NatWest Group plc — Valuation Comparison 2026

NSTS

Banks - Regional
NSTS Bancorp, Inc.
Quality
6.2
out of 10
Value Trap
12
SAFE
Price
$13.77
Last close
Models
12/13
Active
VS

NWG

Banks - Regional
NatWest Group plc
Quality
7.6
out of 10
Value Trap
22
SAFE
Price
$15.83
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType NSTS Fair ValueNSTS Upside NWG Fair ValueNWG Upside
Bayesian DCF Intrinsic $15.58 +13.2% $68.56 +333.1%
Earnings Power Value Intrinsic $10.16 -26.2% $76.02 +380.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 NSTS vs NWG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NSTS vs NWG — Which Stock Is More Undervalued?

NWG scores higher with a 7.6/10 quality rating vs NSTS's 6.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing NSTS Bancorp, Inc. (NSTS) and NatWest Group plc (NWG) 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.

NSTS currently trades at $13.77 with a QOC of 6.2/10, while NWG trades at $15.83 with a QOC of 7.6/10.

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).