USB vs VLY

U.S. Bancorp vs Valley National Bancorp — Valuation Comparison 2026

USB

Banks - Regional
U.S. Bancorp
Quality
6.7
out of 10
Value Trap
20
SAFE
Price
$54.45
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 USB Fair ValueUSB Upside VLY Fair ValueVLY Upside
Bayesian DCF Intrinsic $25.21 -53.7% $3.17 -76.9%
Earnings Power Value Intrinsic $64.32 +18.1% $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 USB vs VLY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

USB vs VLY — Which Stock Is More Undervalued?

VLY scores higher with a 7.5/10 quality rating vs USB's 6.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing U.S. Bancorp (USB) 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.

USB currently trades at $54.45 with a QOC of 6.7/10, 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).