GD vs GPUS

General Dynamics Corporation vs Hyperscale Data, Inc. — Valuation Comparison 2026

GD

Aerospace & Defense
General Dynamics Corporation
Quality
8.8
out of 10
Value Trap
Price
$348.96
Last close
Models
13/13
Active
VS

GPUS

Aerospace & Defense
Hyperscale Data, Inc.
Quality
3.1
out of 10
Value Trap
18
SAFE
Price
$0.19
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType GD Fair ValueGD Upside GPUS Fair ValueGPUS Upside
Bayesian DCF Intrinsic $273.55 -21.6%
Earnings Power Value Intrinsic $101.06 -71.0% $0.32 +132.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $103.96 -70.2% $0.34 +82.9%
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 GD vs GPUS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GD vs GPUS — Which Stock Is More Undervalued?

GD scores higher with a 8.8/10 quality rating vs GPUS's 3.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing General Dynamics Corporation (GD) and Hyperscale Data, Inc. (GPUS) 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.

GD currently trades at $348.96 with a QOC of 8.8/10, while GPUS trades at $0.19 with a QOC of 3.1/10.

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).