RACE vs STLA

Ferrari N.V. vs Stellantis N.V. — Valuation Comparison 2026

RACE

Auto Manufacturers
Ferrari N.V.
Quality
10.0
out of 10
Value Trap
Price
$346.35
Last close
Models
13/13
Active
VS

STLA

Auto Manufacturers
Stellantis N.V.
Quality
5.5
out of 10
Value Trap
6
SAFE
Price
$8.20
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType RACE Fair ValueRACE Upside STLA Fair ValueSTLA Upside
Bayesian DCF Intrinsic $55.07 -84.1%
Earnings Power Value Intrinsic $133.62 -61.4% $19.67 +175.8%
EROIC Spread Intrinsic $67.03 -80.6% $35.10 +335.5%
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 RACE vs STLA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RACE vs STLA — Which Stock Is More Undervalued?

RACE scores higher with a 10.0/10 quality rating vs STLA's 5.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ferrari N.V. (RACE) and Stellantis N.V. (STLA) 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.

RACE currently trades at $346.35 with a QOC of 10.0/10, while STLA trades at $8.20 with a QOC of 5.5/10.

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).