SPFI vs SRBK

South Plains Financial, Inc. vs SR Bancorp, Inc. — Valuation Comparison 2026

SPFI

Banks - Regional
South Plains Financial, Inc.
Quality
9.0
out of 10
Value Trap
20
SAFE
Price
$40.44
Last close
Models
12/13
Active
VS

SRBK

Banks - Regional
SR Bancorp, Inc.
Quality
8.1
out of 10
Value Trap
Price
$18.82
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType SPFI Fair ValueSPFI Upside SRBK Fair ValueSRBK Upside
Bayesian DCF Intrinsic $56.38 +39.4% $7.20 -61.7%
Earnings Power Value Intrinsic $82.52 +104.1% $12.76 -32.2%
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 SPFI vs SRBK — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SPFI vs SRBK — Which Stock Is More Undervalued?

SPFI scores higher with a 9.0/10 quality rating vs SRBK's 8.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing South Plains Financial, Inc. (SPFI) and SR Bancorp, Inc. (SRBK) 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.

SPFI currently trades at $40.44 with a QOC of 9.0/10, while SRBK trades at $18.82 with a QOC of 8.1/10.

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).