MGYR vs MSBI

Magyar Bancorp, Inc. vs Midland States Bancorp, Inc. — Valuation Comparison 2026

MGYR

Banks - Regional
Magyar Bancorp, Inc.
Quality
9.0
out of 10
Value Trap
20
SAFE
Price
$16.97
Last close
Models
11/13
Active
VS

MSBI

Banks - Regional
Midland States Bancorp, Inc.
Quality
7.6
out of 10
Value Trap
14
SAFE
Price
$27.75
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType MGYR Fair ValueMGYR Upside MSBI Fair ValueMSBI Upside
Bayesian DCF Intrinsic $7.83 -53.9% $23.87 -14.0%
Earnings Power Value Intrinsic $16.25 -4.2% $73.22 +163.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 MGYR vs MSBI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MGYR vs MSBI — Which Stock Is More Undervalued?

MGYR scores higher with a 9.0/10 quality rating vs MSBI's 7.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Magyar Bancorp, Inc. (MGYR) and Midland States Bancorp, Inc. (MSBI) 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.

MGYR currently trades at $16.97 with a QOC of 9.0/10, while MSBI trades at $27.75 with a QOC of 7.6/10.

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).