BCAL vs BFC

California BanCorp vs Bank First Corporation — Valuation Comparison 2026

BCAL

Banks - Regional
California BanCorp
Quality
10.0
out of 10
Value Trap
18
SAFE
Price
$18.94
Last close
Models
11/13
Active
VS

BFC

Banks - Regional
Bank First Corporation
Quality
8.8
out of 10
Value Trap
Price
$140.83
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType BCAL Fair ValueBCAL Upside BFC Fair ValueBFC Upside
Bayesian DCF Intrinsic $20.05 +5.9% $58.39 -58.5%
Earnings Power Value Intrinsic $29.01 +53.2% $83.95 -40.4%
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 BCAL vs BFC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BCAL vs BFC — Which Stock Is More Undervalued?

BCAL scores higher with a 10.0/10 quality rating vs BFC's 8.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing California BanCorp (BCAL) and Bank First Corporation (BFC) 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.

BCAL currently trades at $18.94 with a QOC of 10.0/10, while BFC trades at $140.83 with a QOC of 8.8/10.

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).