ISBA vs ISTR

Isabella Bank Corporation vs Investar Holding Corporation — Valuation Comparison 2026

ISBA

Banks - Regional
Isabella Bank Corporation
Quality
8.7
out of 10
Value Trap
12
SAFE
Price
$41.13
Last close
Models
11/13
Active
VS

ISTR

Banks - Regional
Investar Holding Corporation
Quality
7.2
out of 10
Value Trap
12
SAFE
Price
$28.28
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType ISBA Fair ValueISBA Upside ISTR Fair ValueISTR Upside
Bayesian DCF Intrinsic $41.52 +0.9% $12.07 -57.3%
Earnings Power Value Intrinsic $27.98 -32.0% $19.83 -29.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-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 ISBA vs ISTR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ISBA vs ISTR — Which Stock Is More Undervalued?

ISBA scores higher with a 8.7/10 quality rating vs ISTR's 7.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Isabella Bank Corporation (ISBA) and Investar Holding Corporation (ISTR) 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.

ISBA currently trades at $41.13 with a QOC of 8.7/10, while ISTR trades at $28.28 with a QOC of 7.2/10.

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).