USCB vs VLY

USCB Financial Holdings, Inc. vs Valley National Bancorp — Valuation Comparison 2026

USCB

Banks - Regional
USCB Financial Holdings, Inc.
Quality
9.1
out of 10
Value Trap
12
SAFE
Price
$18.54
Last close
Models
12/13
Active
VS

VLY

Banks - Regional
Valley National Bancorp
Quality
7.5
out of 10
Value Trap
20
SAFE
Price
$13.72
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType USCB Fair ValueUSCB Upside VLY Fair ValueVLY Upside
Bayesian DCF Intrinsic $14.98 -19.2% $3.17 -76.9%
Earnings Power Value Intrinsic $17.22 -7.1% $11.32 -17.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-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 USCB vs VLY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

USCB vs VLY — Which Stock Is More Undervalued?

USCB scores higher with a 9.1/10 quality rating vs VLY's 7.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing USCB Financial Holdings, Inc. (USCB) and Valley National Bancorp (VLY) 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.

USCB currently trades at $18.54 with a QOC of 9.1/10, while VLY trades at $13.72 with a QOC of 7.5/10.

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).