UMBFO vs VLY

UMB Financial Corporation - Dep vs Valley National Bancorp — Valuation Comparison 2026

UMBFO

National Commercial Banks
UMB Financial Corporation - Dep
Quality
7.7
out of 10
Value Trap
26
LOW
Price
$26.94
Last close
Models
4/13
Active
VS

VLY

National Commercial Banks
Valley National Bancorp
Quality
7.5
out of 10
Value Trap
20
SAFE
Price
$13.77
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType UMBFO Fair ValueUMBFO Upside VLY Fair ValueVLY Upside
Bayesian DCF Intrinsic $3.17 -77.0%
Earnings Power Value Intrinsic $11.32 -17.8%
EROIC Spread Intrinsic $132.71 +392.6% $8.40 -39.0%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $35.21 +30.7% $4.55 -67.0%
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 UMBFO vs VLY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

UMBFO vs VLY — Which Stock Is More Undervalued?

UMBFO scores higher with a 7.7/10 quality rating vs VLY's 7.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing UMB Financial Corporation - Dep (UMBFO) 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.

UMBFO currently trades at $26.94 with a QOC of 7.7/10, while VLY trades at $13.77 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).