CXT vs PH

Crane NXT, Co. vs Parker-Hannifin Corporation — Valuation Comparison 2026

CXT

Miscellaneous Fabricated Metal Products
Crane NXT, Co.
Quality
8.7
out of 10
Value Trap
29
LOW
Price
$38.84
Last close
Models
12/13
Active
VS

PH

Miscellaneous Fabricated Metal Products
Parker-Hannifin Corporation
Quality
10.0
out of 10
Value Trap
11
SAFE
Price
$844.63
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CXT Fair ValueCXT Upside PH Fair ValuePH Upside
Bayesian DCF Intrinsic $28.33 -27.1% $372.05 -56.0%
Earnings Power Value Intrinsic $18.50 -52.4% $179.46 -78.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 CXT vs PH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CXT vs PH — Which Stock Is More Undervalued?

PH scores higher with a 10.0/10 quality rating vs CXT's 8.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Crane NXT, Co. (CXT) and Parker-Hannifin Corporation (PH) 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.

CXT currently trades at $38.84 with a QOC of 8.7/10, while PH trades at $844.63 with a QOC of 10.0/10.

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).