GGG vs GRC

Graco Inc. vs Gorman-Rupp Company (The) — Valuation Comparison 2026

GGG

Pumps & Pumping Equipment
Graco Inc.
Quality
9.8
out of 10
Value Trap
6
SAFE
Price
$75.45
Last close
Models
13/13
Active
VS

GRC

Pumps & Pumping Equipment
Gorman-Rupp Company (The)
Quality
9.1
out of 10
Value Trap
25
LOW
Price
$74.95
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType GGG Fair ValueGGG Upside GRC Fair ValueGRC Upside
Bayesian DCF Intrinsic $37.70 -50.0% $14.81 -80.2%
Earnings Power Value Intrinsic $35.20 -53.3% $17.61 -76.5%
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 GGG vs GRC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GGG vs GRC — Which Stock Is More Undervalued?

GGG scores higher with a 9.8/10 quality rating vs GRC's 9.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Graco Inc. (GGG) and Gorman-Rupp Company (The) (GRC) 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.

GGG currently trades at $75.45 with a QOC of 9.8/10, while GRC trades at $74.95 with a QOC of 9.1/10.

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).