CBU vs CCNEP

Community Financial System, Inc vs CNB Financial Corporation - Dep — Valuation Comparison 2026

CBU

Banks - Regional
Community Financial System, Inc
Quality
8.3
out of 10
Value Trap
Price
$64.30
Last close
Models
12/13
Active
VS

CCNEP

Banks - Regional
CNB Financial Corporation - Dep
Quality
8.5
out of 10
Value Trap
10
SAFE
Price
$24.82
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CBU Fair ValueCBU Upside CCNEP Fair ValueCCNEP Upside
Bayesian DCF Intrinsic $26.91 -58.2% $18.89 -23.9%
Earnings Power Value Intrinsic $42.98 -33.2% $41.72 +68.1%
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 CBU vs CCNEP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CBU vs CCNEP — Which Stock Is More Undervalued?

CCNEP scores higher with a 8.5/10 quality rating vs CBU's 8.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Community Financial System, Inc (CBU) and CNB Financial Corporation - Dep (CCNEP) 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.

CBU currently trades at $64.30 with a QOC of 8.3/10, while CCNEP trades at $24.82 with a QOC of 8.5/10.

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).