CHYM vs CNF

Chime Financial, Inc. vs CNFinance Holdings Limited — Valuation Comparison 2026

CHYM

Finance Services
Chime Financial, Inc.
Quality
6.2
out of 10
Value Trap
Price
$18.60
Last close
Models
13/13
Active
VS

CNF

Finance Services
CNFinance Holdings Limited
Quality
5.5
out of 10
Value Trap
39
LOW
Price
$3.13
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType CHYM Fair ValueCHYM Upside CNF Fair ValueCNF Upside
Bayesian DCF Intrinsic $1.52 -91.4%
Earnings Power Value Intrinsic $12.86 -42.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $2.06 -88.9% $0.33 -89.1%
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 $3.15 -83.1% $10.86 +247.1%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for CHYM vs CNF — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CHYM vs CNF — Which Stock Is More Undervalued?

CHYM scores higher with a 6.2/10 quality rating vs CNF's 5.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Chime Financial, Inc. (CHYM) and CNFinance Holdings Limited (CNF) 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.

CHYM currently trades at $18.60 with a QOC of 6.2/10, while CNF trades at $3.13 with a QOC of 5.5/10.

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).