OPBK vs OSBC

OP Bancorp vs Old Second Bancorp, Inc. — Valuation Comparison 2026

OPBK

Banks - Regional
OP Bancorp
Quality
9.6
out of 10
Value Trap
16
SAFE
Price
$14.06
Last close
Models
11/13
Active
VS

OSBC

Banks - Regional
Old Second Bancorp, Inc.
Quality
8.8
out of 10
Value Trap
6
SAFE
Price
$21.30
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType OPBK Fair ValueOPBK Upside OSBC Fair ValueOSBC Upside
Bayesian DCF Intrinsic $22.80 +62.2% $8.09 -62.0%
Earnings Power Value Intrinsic $30.84 +119.4% $12.97 -39.1%
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 OPBK vs OSBC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

OPBK vs OSBC — Which Stock Is More Undervalued?

OPBK scores higher with a 9.6/10 quality rating vs OSBC's 8.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing OP Bancorp (OPBK) and Old Second Bancorp, Inc. (OSBC) 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.

OPBK currently trades at $14.06 with a QOC of 9.6/10, while OSBC trades at $21.30 with a QOC of 8.8/10.

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).