HON vs NNBR

Honeywell International Inc. vs NN, Inc. — Valuation Comparison 2026

HON

Conglomerates
Honeywell International Inc.
Quality
8.6
out of 10
Value Trap
8
SAFE
Price
$233.00
Last close
Models
13/13
Active
VS

NNBR

Conglomerates
NN, Inc.
Quality
5.4
out of 10
Value Trap
26
LOW
Price
$3.08
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType HON Fair ValueHON Upside NNBR Fair ValueNNBR Upside
Bayesian DCF Intrinsic $55.09 -76.4%
Earnings Power Value Intrinsic $62.59 -73.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $70.94 -69.6% $0.44 -85.6%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $3.36 -98.6% $0.40 -87.0%
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 HON vs NNBR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HON vs NNBR — Which Stock Is More Undervalued?

HON scores higher with a 8.6/10 quality rating vs NNBR's 5.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Honeywell International Inc. (HON) and NN, Inc. (NNBR) 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.

HON currently trades at $233.00 with a QOC of 8.6/10, while NNBR trades at $3.08 with a QOC of 5.4/10.

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).