AWI vs CSTE

Armstrong World Industries Inc vs Caesarstone Ltd. — Valuation Comparison 2026

AWI

Building Products & Equipment
Armstrong World Industries Inc
Quality
9.6
out of 10
Value Trap
Price
$160.34
Last close
Models
13/13
Active
VS

CSTE

Building Products & Equipment
Caesarstone Ltd.
Quality
2.5
out of 10
Value Trap
Price
$1.82
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType AWI Fair ValueAWI Upside CSTE Fair ValueCSTE Upside
Bayesian DCF Intrinsic $47.85 -70.2% $0.36 -80.2%
Earnings Power Value Intrinsic $71.43 -55.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $74.52 -53.5% $3.22 +76.7%
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 AWI vs CSTE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AWI vs CSTE — Which Stock Is More Undervalued?

AWI scores higher with a 9.6/10 quality rating vs CSTE's 2.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Armstrong World Industries Inc (AWI) and Caesarstone Ltd. (CSTE) 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.

AWI currently trades at $160.34 with a QOC of 9.6/10, while CSTE trades at $1.82 with a QOC of 2.5/10.

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).