CUBI vs CVBF

Customers Bancorp, Inc vs CVB Financial Corporation — Valuation Comparison 2026

CUBI

Banks - Regional
Customers Bancorp, Inc
Quality
8.9
out of 10
Value Trap
12
SAFE
Price
$75.37
Last close
Models
11/13
Active
VS

CVBF

Banks - Regional
CVB Financial Corporation
Quality
8.0
out of 10
Value Trap
8
SAFE
Price
$20.41
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CUBI Fair ValueCUBI Upside CVBF Fair ValueCVBF Upside
Bayesian DCF Intrinsic $188.85 +150.6% $9.47 -53.6%
Earnings Power Value Intrinsic $288.54 +282.8% $9.61 -52.9%
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 CUBI vs CVBF — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CUBI vs CVBF — Which Stock Is More Undervalued?

CUBI scores higher with a 8.9/10 quality rating vs CVBF's 8.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Customers Bancorp, Inc (CUBI) and CVB Financial Corporation (CVBF) 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.

CUBI currently trades at $75.37 with a QOC of 8.9/10, while CVBF trades at $20.41 with a QOC of 8.0/10.

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).