COF vs ENVA

Capital One Financial Corporati vs Enova International, Inc. — Valuation Comparison 2026

COF

Credit Services
Capital One Financial Corporati
Quality
7.4
out of 10
Value Trap
20
SAFE
Price
$187.02
Last close
Models
12/13
Active
VS

ENVA

Credit Services
Enova International, Inc.
Quality
9.2
out of 10
Value Trap
12
SAFE
Price
$158.90
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType COF Fair ValueCOF Upside ENVA Fair ValueENVA Upside
Bayesian DCF Intrinsic $191.49 +2.4%
Earnings Power Value Intrinsic $155.75 -16.7%
EROIC Spread Intrinsic $94.58 -49.4% $50.61 -68.2%
First Chicago Scenario $186.17 -0.5% $355.49 +123.7%
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 COF vs ENVA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

COF vs ENVA — Which Stock Is More Undervalued?

ENVA scores higher with a 9.2/10 quality rating vs COF's 7.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Capital One Financial Corporati (COF) and Enova International, Inc. (ENVA) 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.

COF currently trades at $187.02 with a QOC of 7.4/10, while ENVA trades at $158.90 with a QOC of 9.2/10.

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).