EA vs GDC

Electronic Arts Inc. vs GD Culture Group Limited — Valuation Comparison 2026

EA

Electronic Gaming & Multimedia
Electronic Arts Inc.
Quality
9.1
out of 10
Value Trap
31
LOW
Price
$201.15
Last close
Models
13/13
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 EA Fair ValueEA Upside GDC Fair ValueGDC Upside
Bayesian DCF Intrinsic $157.00 -21.9% $0.02 -81.5%
Earnings Power Value Intrinsic $23.07 -88.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $208.74 +3.8% $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 EA vs GDC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EA vs GDC — Which Stock Is More Undervalued?

EA scores higher with a 9.1/10 quality rating vs GDC's 3.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Electronic Arts Inc. (EA) 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.

EA currently trades at $201.15 with a QOC of 9.1/10, 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).