ENOV vs INGN

Enovis Corporation vs Inogen, Inc — Valuation Comparison 2026

ENOV

Orthopedic, Prosthetic & Surgical Appliances & Supplies
Enovis Corporation
Quality
5.0
out of 10
Value Trap
37
LOW
Price
$22.68
Last close
Models
10/13
Active
VS

INGN

Orthopedic, Prosthetic & Surgical Appliances & Supplies
Inogen, Inc
Quality
6.6
out of 10
Value Trap
26
LOW
Price
$6.49
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType ENOV Fair ValueENOV Upside INGN Fair ValueINGN Upside
Bayesian DCF Intrinsic $13.78 -39.2% $2.83 -56.4%
Earnings Power Value Intrinsic $4.62 -79.7% $9.45 +45.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for ENOV vs INGN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ENOV vs INGN — Which Stock Is More Undervalued?

INGN scores higher with a 6.6/10 quality rating vs ENOV's 5.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Enovis Corporation (ENOV) and Inogen, Inc (INGN) 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.

ENOV currently trades at $22.68 with a QOC of 5.0/10, while INGN trades at $6.49 with a QOC of 6.6/10.

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).