CP vs UNP

Canadian Pacific Kansas City Li vs Union Pacific Corporation — Valuation Comparison 2026

CP

Railroads, Line-Haul Operating
Canadian Pacific Kansas City Li
Quality
9.2
out of 10
Value Trap
25
LOW
Price
$89.32
Last close
Models
13/13
Active
VS

UNP

Railroads, Line-Haul Operating
Union Pacific Corporation
Quality
8.9
out of 10
Value Trap
Price
$262.64
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CP Fair ValueCP Upside UNP Fair ValueUNP Upside
Bayesian DCF Intrinsic $24.49 -72.6% $65.15 -75.2%
Earnings Power Value Intrinsic $6.12 -93.1% $58.75 -77.6%
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 CP vs UNP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CP vs UNP — Which Stock Is More Undervalued?

CP scores higher with a 9.2/10 quality rating vs UNP's 8.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Canadian Pacific Kansas City Li (CP) and Union Pacific Corporation (UNP) 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.

CP currently trades at $89.32 with a QOC of 9.2/10, while UNP trades at $262.64 with a QOC of 8.9/10.

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).