HMC vs NIO

Honda Motor Company, Ltd. vs NIO 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

NIO

Motor Vehicles & Passenger Car Bodies
NIO Inc.
Quality
6.8
out of 10
Value Trap
18
SAFE
Price
$5.60
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType HMC Fair ValueHMC Upside NIO Fair ValueNIO Upside
Bayesian DCF Intrinsic $8.65 -67.9% $0.53 -90.6%
Earnings Power Value Intrinsic $11.10 -54.4% $6.10 +3.3%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

HMC vs NIO — Which Stock Is More Undervalued?

NIO scores higher with a 6.8/10 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 NIO Inc. (NIO) 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 NIO trades at $5.60 with a QOC of 6.8/10.

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).