CBU vs CFFN

Community Financial System, Inc vs Capitol Federal Financial, Inc. — 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

CFFN

Banks - Regional
Capitol Federal Financial, Inc.
Quality
7.8
out of 10
Value Trap
Price
$7.80
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CBU Fair ValueCBU Upside CFFN Fair ValueCFFN Upside
Bayesian DCF Intrinsic $26.91 -58.2% $4.71 -39.6%
Earnings Power Value Intrinsic $42.98 -33.2% $7.25 -7.0%
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 CFFN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CBU vs CFFN — Which Stock Is More Undervalued?

CBU scores higher with a 8.3/10 quality rating vs CFFN's 7.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Community Financial System, Inc (CBU) and Capitol Federal Financial, Inc. (CFFN) 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 CFFN trades at $7.80 with a QOC of 7.8/10.

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).