DNOW vs FTI

DNOW Inc. vs TechnipFMC plc — Valuation Comparison 2026

DNOW

Oil & Gas Field Machinery & Equipment
DNOW Inc.
Quality
6.5
out of 10
Value Trap
30
LOW
Price
$12.79
Last close
Models
13/13
Active
VS

FTI

Oil & Gas Field Machinery & Equipment
TechnipFMC plc
Quality
9.5
out of 10
Value Trap
12
SAFE
Price
$68.42
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType DNOW Fair ValueDNOW Upside FTI Fair ValueFTI Upside
Bayesian DCF Intrinsic $1.99 -84.5% $38.32 -44.0%
Earnings Power Value Intrinsic $3.48 -73.4% $37.40 -45.3%
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 DNOW vs FTI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

DNOW vs FTI — Which Stock Is More Undervalued?

FTI scores higher with a 9.5/10 quality rating vs DNOW's 6.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing DNOW Inc. (DNOW) and TechnipFMC plc (FTI) 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.

DNOW currently trades at $12.79 with a QOC of 6.5/10, while FTI trades at $68.42 with a QOC of 9.5/10.

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).