ORN vs SHIM

Orion Group Holdings, Inc. vs Shimmick Corporation — Valuation Comparison 2026

ORN

Heavy Construction Other Than Bldg Const - Contractors
Orion Group Holdings, Inc.
Quality
7.5
out of 10
Value Trap
18
SAFE
Price
$13.76
Last close
Models
10/13
Active
VS

SHIM

Heavy Construction Other Than Bldg Const - Contractors
Shimmick Corporation
Quality
4.9
out of 10
Value Trap
19
SAFE
Price
$3.54
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType ORN Fair ValueORN Upside SHIM Fair ValueSHIM Upside
Bayesian DCF Intrinsic $1.70 -87.9% $0.27 -92.3%
Earnings Power Value Intrinsic $0.18 -98.7% $6.28 +16.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

ORN vs SHIM — Which Stock Is More Undervalued?

ORN scores higher with a 7.5/10 quality rating vs SHIM's 4.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Orion Group Holdings, Inc. (ORN) 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.

ORN currently trades at $13.76 with a QOC of 7.5/10, while SHIM trades at $3.54 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).