AX vs BAFN

Axos Financial, Inc. vs BayFirst Financial Corp. — Valuation Comparison 2026

AX

Banks - Regional
Axos Financial, Inc.
Quality
9.0
out of 10
Value Trap
8
SAFE
Price
$87.27
Last close
Models
12/13
Active
VS

BAFN

Banks - Regional
BayFirst Financial Corp.
Quality
6.3
out of 10
Value Trap
27
LOW
Price
$6.13
Last close
Models
4/13
Active

Model-by-Model Comparison

ModelType AX Fair ValueAX Upside BAFN Fair ValueBAFN Upside
Bayesian DCF Intrinsic $45.33 -48.1%
Earnings Power Value Intrinsic $77.30 -11.4% $35.39 +455.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $113.12 +29.6% $1.40 -77.1%
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 AX vs BAFN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AX vs BAFN — Which Stock Is More Undervalued?

AX scores higher with a 9.0/10 quality rating vs BAFN's 6.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Axos Financial, Inc. (AX) and BayFirst Financial Corp. (BAFN) 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.

AX currently trades at $87.27 with a QOC of 9.0/10, while BAFN trades at $6.13 with a QOC of 6.3/10.

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).