CPSS vs FINV

Consumer Portfolio Services, In vs FinVolution Group — Valuation Comparison 2026

CPSS

Credit Services
Consumer Portfolio Services, In
Quality
8.8
out of 10
Value Trap
Price
$9.84
Last close
Models
7/13
Active
VS

FINV

Credit Services
FinVolution Group
Quality
9.2
out of 10
Value Trap
15
SAFE
Price
$5.19
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType CPSS Fair ValueCPSS Upside FINV Fair ValueFINV Upside
Bayesian DCF Intrinsic $38.30 +289.2% $8.29 +59.8%
Earnings Power Value Intrinsic $9.99 +5.2%
EROIC Spread Intrinsic $26.80 +416.4%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
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 CPSS vs FINV — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CPSS vs FINV — Which Stock Is More Undervalued?

FINV scores higher with a 9.2/10 quality rating vs CPSS's 8.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Consumer Portfolio Services, In (CPSS) and FinVolution Group (FINV) 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.

CPSS currently trades at $9.84 with a QOC of 8.8/10, while FINV trades at $5.19 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).