KMB vs ODD

Kimberly-Clark Corporation vs ODDITY Tech Ltd. — Valuation Comparison 2026

KMB

Household & Personal Products
Kimberly-Clark Corporation
Quality
8.5
out of 10
Value Trap
14
SAFE
Price
$100.14
Last close
Models
13/13
Active
VS

ODD

Household & Personal Products
ODDITY Tech Ltd.
Quality
10.0
out of 10
Value Trap
18
SAFE
Price
$13.10
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType KMB Fair ValueKMB Upside ODD Fair ValueODD Upside
Bayesian DCF Intrinsic $47.67 -52.4% $24.31 +85.6%
Earnings Power Value Intrinsic $30.35 -69.7% $11.11 -15.2%
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 KMB vs ODD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KMB vs ODD — Which Stock Is More Undervalued?

ODD scores higher with a 10.0/10 quality rating vs KMB's 8.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Kimberly-Clark Corporation (KMB) and ODDITY Tech Ltd. (ODD) 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.

KMB currently trades at $100.14 with a QOC of 8.5/10, while ODD trades at $13.10 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).