CNOBP vs COFS

ConnectOne Bancorp, Inc. - Depo vs ChoiceOne Financial Services, I — Valuation Comparison 2026

CNOBP

Banks - Regional
ConnectOne Bancorp, Inc. - Depo
Quality
8.1
out of 10
Value Trap
6
SAFE
Price
$24.84
Last close
Models
11/13
Active
VS

COFS

Banks - Regional
ChoiceOne Financial Services, I
Quality
8.2
out of 10
Value Trap
25
LOW
Price
$31.42
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CNOBP Fair ValueCNOBP Upside COFS Fair ValueCOFS Upside
Bayesian DCF Intrinsic $16.78 -32.5% $14.67 -53.3%
Earnings Power Value Intrinsic $28.03 +12.9% $38.58 +22.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 CNOBP vs COFS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CNOBP vs COFS — Which Stock Is More Undervalued?

COFS scores higher with a 8.2/10 quality rating vs CNOBP's 8.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing ConnectOne Bancorp, Inc. - Depo (CNOBP) and ChoiceOne Financial Services, I (COFS) 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.

CNOBP currently trades at $24.84 with a QOC of 8.1/10, while COFS trades at $31.42 with a QOC of 8.2/10.

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).