VC vs WPRT

Visteon Corporation vs Westport Fuel Systems Inc — Valuation Comparison 2026

VC

Auto Parts
Visteon Corporation
Quality
7.7
out of 10
Value Trap
18
SAFE
Price
$119.12
Last close
Models
13/13
Active
VS

WPRT

Auto Parts
Westport Fuel Systems Inc
Quality
2.0
out of 10
Value Trap
Price
$2.00
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType VC Fair ValueVC Upside WPRT Fair ValueWPRT Upside
Bayesian DCF Intrinsic $174.52 +46.5% $0.53 -73.5%
Earnings Power Value Intrinsic $64.93 -45.5% $5.23 +164.3%
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 VC vs WPRT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

VC vs WPRT — Which Stock Is More Undervalued?

VC scores higher with a 7.7/10 quality rating vs WPRT's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Visteon Corporation (VC) and Westport Fuel Systems Inc (WPRT) 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.

VC currently trades at $119.12 with a QOC of 7.7/10, while WPRT trades at $2.00 with a QOC of 2.0/10.

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).