CBNA vs CFBK

Chain Bridge Bancorp, Inc. vs CF Bankshares Inc. — Valuation Comparison 2026

CBNA

National Commercial Banks
Chain Bridge Bancorp, Inc.
Quality
9.2
out of 10
Value Trap
Price
$36.25
Last close
Models
10/13
Active
VS

CFBK

National Commercial Banks
CF Bankshares Inc.
Quality
8.6
out of 10
Value Trap
25
LOW
Price
$28.52
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CBNA Fair ValueCBNA Upside CFBK Fair ValueCFBK Upside
Bayesian DCF Intrinsic $130.96 +261.3% $84.54 +196.4%
Earnings Power Value Intrinsic $160.10 +341.7% $79.38 +178.3%
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 CBNA vs CFBK — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CBNA vs CFBK — Which Stock Is More Undervalued?

CBNA scores higher with a 9.2/10 quality rating vs CFBK's 8.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Chain Bridge Bancorp, Inc. (CBNA) and CF Bankshares Inc. (CFBK) 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.

CBNA currently trades at $36.25 with a QOC of 9.2/10, while CFBK trades at $28.52 with a QOC of 8.6/10.

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).