CLST vs COFS

Catalyst Bancorp, Inc. vs ChoiceOne Financial Services, I — Valuation Comparison 2026

CLST

Banks - Regional
Catalyst Bancorp, Inc.
Quality
8.9
out of 10
Value Trap
12
SAFE
Price
$15.92
Last close
Models
12/13
Active
VS

COFS

Banks - Regional
ChoiceOne Financial Services, I
Quality
8.2
out of 10
Value Trap
25
LOW
Price
$31.42
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType CLST Fair ValueCLST Upside COFS Fair ValueCOFS Upside
Bayesian DCF Intrinsic $11.18 -29.8% $14.67 -53.3%
Earnings Power Value Intrinsic $12.46 -21.7% $38.58 +22.8%
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 CLST vs COFS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CLST vs COFS — Which Stock Is More Undervalued?

CLST scores higher with a 8.9/10 quality rating vs COFS's 8.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Catalyst Bancorp, Inc. (CLST) and ChoiceOne Financial Services, I (COFS) 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.

CLST currently trades at $15.92 with a QOC of 8.9/10, while COFS trades at $31.42 with a QOC of 8.2/10.

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).