HMC vs LOT

Honda Motor Company, Ltd. vs Lotus Technology Inc. — Valuation Comparison 2026

HMC

Motor Vehicles & Passenger Car Bodies
Honda Motor Company, Ltd.
Quality
1.9
out of 10
Value Trap
Price
$26.99
Last close
Models
8/13
Active
VS

LOT

Motor Vehicles & Passenger Car Bodies
Lotus Technology Inc.
Quality
2.6
out of 10
Value Trap
Price
$1.28
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType HMC Fair ValueHMC Upside LOT Fair ValueLOT Upside
Bayesian DCF Intrinsic $8.65 -67.9% $0.37 -71.0%
Earnings Power Value Intrinsic $11.10 -54.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $3.88 +203.0%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for HMC vs LOT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HMC vs LOT — Which Stock Is More Undervalued?

LOT scores higher with a 2.6/10 quality rating vs HMC's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Honda Motor Company, Ltd. (HMC) and Lotus Technology Inc. (LOT) 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.

HMC currently trades at $26.99 with a QOC of 1.9/10, while LOT trades at $1.28 with a QOC of 2.6/10.

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).