PCAR vs SCAG

PACCAR Inc. vs Scage Future — Valuation Comparison 2026

PCAR

Farm & Heavy Construction Machinery
PACCAR Inc.
Quality
8.6
out of 10
Value Trap
12
SAFE
Price
$112.22
Last close
Models
13/13
Active
VS

SCAG

Farm & Heavy Construction Machinery
Scage Future
Quality
1.9
out of 10
Value Trap
Price
$0.56
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType PCAR Fair ValuePCAR Upside SCAG Fair ValueSCAG Upside
Bayesian DCF Intrinsic $113.20 +0.9% $0.09 -84.3%
Earnings Power Value Intrinsic $39.58 -64.7%
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 $143.80 +28.1% $0.15 -74.7%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PCAR vs SCAG — Which Stock Is More Undervalued?

PCAR scores higher with a 8.6/10 quality rating vs SCAG's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing PACCAR Inc. (PCAR) and Scage Future (SCAG) 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.

PCAR currently trades at $112.22 with a QOC of 8.6/10, while SCAG trades at $0.56 with a QOC of 1.9/10.

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).