BVFL vs BY

BV Financial, Inc. vs Byline Bancorp, Inc. — Valuation Comparison 2026

BVFL

Banks - Regional
BV Financial, Inc.
Quality
8.6
out of 10
Value Trap
20
SAFE
Price
$19.91
Last close
Models
12/13
Active
VS

BY

Banks - Regional
Byline Bancorp, Inc.
Quality
9.0
out of 10
Value Trap
20
SAFE
Price
$33.14
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType BVFL Fair ValueBVFL Upside BY Fair ValueBY Upside
Bayesian DCF Intrinsic $17.41 -12.6% $13.03 -60.7%
Earnings Power Value Intrinsic $14.43 -27.5% $14.36 -56.7%
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 BVFL vs BY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BVFL vs BY — Which Stock Is More Undervalued?

BY scores higher with a 9.0/10 quality rating vs BVFL's 8.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing BV Financial, Inc. (BVFL) and Byline Bancorp, Inc. (BY) 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.

BVFL currently trades at $19.91 with a QOC of 8.6/10, while BY trades at $33.14 with a QOC of 9.0/10.

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).