GD vs HEI

General Dynamics Corporation vs Heico Corporation — 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

HEI

Aerospace & Defense
Heico Corporation
Quality
9.6
out of 10
Value Trap
31
LOW
Price
$345.07
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType GD Fair ValueGD Upside HEI Fair ValueHEI Upside
Bayesian DCF Intrinsic $273.55 -21.6% $34.68 -89.9%
Earnings Power Value Intrinsic $101.06 -71.0% $52.27 -84.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 GD vs HEI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GD vs HEI — Which Stock Is More Undervalued?

HEI scores higher with a 9.6/10 quality rating vs GD's 8.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing General Dynamics Corporation (GD) and Heico Corporation (HEI) 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 HEI trades at $345.07 with a QOC of 9.6/10.

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).