OMF vs PMTS

OneMain Holdings, Inc. vs CPI Card Group Inc. — Valuation Comparison 2026

OMF

Credit Services
OneMain Holdings, Inc.
Quality
7.6
out of 10
Value Trap
20
SAFE
Price
$54.06
Last close
Models
6/13
Active
VS

PMTS

Credit Services
CPI Card Group Inc.
Quality
7.9
out of 10
Value Trap
20
SAFE
Price
$17.52
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType OMF Fair ValueOMF Upside PMTS Fair ValuePMTS Upside
Bayesian DCF Intrinsic $42.23 -21.9% $51.61 +194.6%
Earnings Power Value Intrinsic $23.32 +34.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $101.88 +88.5% $13.11 -25.2%
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 OMF vs PMTS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

OMF vs PMTS — Which Stock Is More Undervalued?

PMTS scores higher with a 7.9/10 quality rating vs OMF's 7.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing OneMain Holdings, Inc. (OMF) and CPI Card Group Inc. (PMTS) 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.

OMF currently trades at $54.06 with a QOC of 7.6/10, while PMTS trades at $17.52 with a QOC of 7.9/10.

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).