DKI vs GDC

DarkIris Inc. vs GD Culture Group Limited — Valuation Comparison 2026

DKI

Electronic Gaming & Multimedia
DarkIris Inc.
Quality
5.1
out of 10
Value Trap
Price
$6.35
Last close
Models
12/13
Active
VS

GDC

Electronic Gaming & Multimedia
GD Culture Group Limited
Quality
3.6
out of 10
Value Trap
30
LOW
Price
$0.12
Last close
Models
3/13
Active

Model-by-Model Comparison

ModelType DKI Fair ValueDKI Upside GDC Fair ValueGDC Upside
Bayesian DCF Intrinsic $2.16 -66.1% $0.02 -81.5%
Earnings Power Value Intrinsic $0.34 -31.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $3.87 -39.0% $0.14 +14.3%
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 DKI vs GDC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

DKI vs GDC — Which Stock Is More Undervalued?

DKI scores higher with a 5.1/10 quality rating vs GDC's 3.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing DarkIris Inc. (DKI) and GD Culture Group Limited (GDC) 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.

DKI currently trades at $6.35 with a QOC of 5.1/10, while GDC trades at $0.12 with a QOC of 3.6/10.

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).