MSBIP vs MTB

Midland States Bancorp, Inc. - vs M&T Bank Corporation — Valuation Comparison 2026

MSBIP

Banks - Regional
Midland States Bancorp, Inc. -
Quality
7.5
out of 10
Value Trap
14
SAFE
Price
$25.44
Last close
Models
11/13
Active
VS

MTB

Banks - Regional
M&T Bank Corporation
Quality
8.5
out of 10
Value Trap
14
SAFE
Price
$214.31
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType MSBIP Fair ValueMSBIP Upside MTB Fair ValueMTB Upside
Bayesian DCF Intrinsic $23.78 -6.5% $115.71 -46.0%
Earnings Power Value Intrinsic $89.73 +252.7% $182.07 -15.0%
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 MSBIP vs MTB — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MSBIP vs MTB — Which Stock Is More Undervalued?

MTB scores higher with a 8.5/10 quality rating vs MSBIP's 7.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Midland States Bancorp, Inc. - (MSBIP) and M&T Bank Corporation (MTB) 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.

MSBIP currently trades at $25.44 with a QOC of 7.5/10, while MTB trades at $214.31 with a QOC of 8.5/10.

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).