PHOE vs SHIM

Phoenix Asia Holdings Limited vs Shimmick Corporation — Valuation Comparison 2026

PHOE

Engineering & Construction
Phoenix Asia Holdings Limited
Quality
2.3
out of 10
Value Trap
Price
$16.29
Last close
Models
12/13
Active
VS

SHIM

Engineering & Construction
Shimmick Corporation
Quality
4.9
out of 10
Value Trap
19
SAFE
Price
$3.57
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType PHOE Fair ValuePHOE Upside SHIM Fair ValueSHIM Upside
Bayesian DCF Intrinsic $4.30 -73.6% $0.20 -94.4%
Earnings Power Value Intrinsic $0.67 -96.2% $6.28 +16.4%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PHOE vs SHIM — Which Stock Is More Undervalued?

SHIM scores higher with a 4.9/10 quality rating vs PHOE's 2.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Phoenix Asia Holdings Limited (PHOE) and Shimmick Corporation (SHIM) 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.

PHOE currently trades at $16.29 with a QOC of 2.3/10, while SHIM trades at $3.57 with a QOC of 4.9/10.

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).