EZGO vs FOXF

EZGO Technologies Ltd. vs Fox Factory Holding Corp. — Valuation Comparison 2026

EZGO

Motorcycles, Bicycles & Parts
EZGO Technologies Ltd.
Quality
1.6
out of 10
Value Trap
6
SAFE
Price
$1.55
Last close
Models
5/13
Active
VS

FOXF

Motorcycles, Bicycles & Parts
Fox Factory Holding Corp.
Quality
7.3
out of 10
Value Trap
12
SAFE
Price
$18.04
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType EZGO Fair ValueEZGO Upside FOXF Fair ValueFOXF Upside
Bayesian DCF Intrinsic $0.36 -77.1% $3.66 -79.7%
Earnings Power Value Intrinsic $90.71 +402.8%
EROIC Spread Intrinsic $0.26 +63.0% $42.73 +136.8%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 EZGO vs FOXF — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EZGO vs FOXF — Which Stock Is More Undervalued?

FOXF scores higher with a 7.3/10 quality rating vs EZGO's 1.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing EZGO Technologies Ltd. (EZGO) and Fox Factory Holding Corp. (FOXF) 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.

EZGO currently trades at $1.55 with a QOC of 1.6/10, while FOXF trades at $18.04 with a QOC of 7.3/10.

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).