CHRW vs EXPD

C.H. Robinson Worldwide, Inc. vs Expeditors International of Was — Valuation Comparison 2026

CHRW

Arrangement of Transportation of Freight & Cargo
C.H. Robinson Worldwide, Inc.
Quality
8.1
out of 10
Value Trap
12
SAFE
Price
$178.65
Last close
Models
12/13
Active
VS

EXPD

Arrangement of Transportation of Freight & Cargo
Expeditors International of Was
Quality
9.2
out of 10
Value Trap
14
SAFE
Price
$157.99
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType CHRW Fair ValueCHRW Upside EXPD Fair ValueEXPD Upside
Bayesian DCF Intrinsic $80.11 -55.2% $133.94 -15.2%
Earnings Power Value Intrinsic $38.43 -78.5% $60.06 -62.0%
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 $•••.•• ••.•% $•••.•• ••.•%
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CHRW vs EXPD — Which Stock Is More Undervalued?

EXPD scores higher with a 9.2/10 quality rating vs CHRW's 8.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing C.H. Robinson Worldwide, Inc. (CHRW) and Expeditors International of Was (EXPD) 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.

CHRW currently trades at $178.65 with a QOC of 8.1/10, while EXPD trades at $157.99 with a QOC of 9.2/10.

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).