SPWR vs WXM

SunPower Inc. vs WF International Limited — Valuation Comparison 2026

SPWR

Construction - Special Trade Contractors
SunPower Inc.
Quality
4.0
out of 10
Value Trap
49
WARN
Price
$1.06
Last close
Models
10/13
Active
VS

WXM

Construction - Special Trade Contractors
WF International Limited
Quality
6.5
out of 10
Value Trap
Price
$0.46
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType SPWR Fair ValueSPWR Upside WXM Fair ValueWXM Upside
Bayesian DCF Intrinsic $1.69 +57.5% $0.20 -56.4%
Earnings Power Value Intrinsic $0.72 +70.6%
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 $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $5.05 +369.9% $0.74 +32.7%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

SPWR vs WXM — Which Stock Is More Undervalued?

WXM scores higher with a 6.5/10 quality rating vs SPWR's 4.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing SunPower Inc. (SPWR) and WF International Limited (WXM) 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.

SPWR currently trades at $1.06 with a QOC of 4.0/10, while WXM trades at $0.46 with a QOC of 6.5/10.

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).