EVI vs GIC

EVI Industries, Inc. vs Global Industrial Company — Valuation Comparison 2026

EVI

Industrial Distribution
EVI Industries, Inc.
Quality
8.0
out of 10
Value Trap
21
SAFE
Price
$17.68
Last close
Models
12/13
Active
VS

GIC

Industrial Distribution
Global Industrial Company
Quality
9.3
out of 10
Value Trap
19
SAFE
Price
$30.40
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType EVI Fair ValueEVI Upside GIC Fair ValueGIC Upside
Bayesian DCF Intrinsic $11.87 -32.9% $16.63 -45.3%
Earnings Power Value Intrinsic $2.41 -86.4% $11.79 -61.2%
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 EVI vs GIC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EVI vs GIC — Which Stock Is More Undervalued?

GIC scores higher with a 9.3/10 quality rating vs EVI's 8.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing EVI Industries, Inc. (EVI) and Global Industrial Company (GIC) 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.

EVI currently trades at $17.68 with a QOC of 8.0/10, while GIC trades at $30.40 with a QOC of 9.3/10.

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).