GTES vs ZBRA

Gates Industrial Corporation pl vs Zebra Technologies Corporation — Valuation Comparison 2026

GTES

General Industrial Machinery & Equipment
Gates Industrial Corporation pl
Quality
8.5
out of 10
Value Trap
12
SAFE
Price
$25.92
Last close
Models
13/13
Active
VS

ZBRA

General Industrial Machinery & Equipment
Zebra Technologies Corporation
Quality
6.1
out of 10
Value Trap
17
SAFE
Price
$243.63
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType GTES Fair ValueGTES Upside ZBRA Fair ValueZBRA Upside
Bayesian DCF Intrinsic $10.74 -58.6% $15.42 -93.7%
Earnings Power Value Intrinsic $12.33 -52.4% $69.32 -71.5%
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 $•••.•• ••.•% $•••.•• ••.•%
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GTES vs ZBRA — Which Stock Is More Undervalued?

GTES scores higher with a 8.5/10 quality rating vs ZBRA's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Gates Industrial Corporation pl (GTES) and Zebra Technologies Corporation (ZBRA) 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.

GTES currently trades at $25.92 with a QOC of 8.5/10, while ZBRA trades at $243.63 with a QOC of 6.1/10.

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).