NNBR vs SEB

NN, Inc. vs Seaboard Corporation — Valuation Comparison 2026

NNBR

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

SEB

Conglomerates
Seaboard Corporation
Quality
8.6
out of 10
Value Trap
16
SAFE
Price
$5047.22
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NNBR Fair ValueNNBR Upside SEB Fair ValueSEB Upside
Bayesian DCF Intrinsic $221.41 -96.0%
Earnings Power Value Intrinsic $1639.47 -70.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $0.44 -85.6% $2136.82 -57.7%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.40 -87.0% $2429.60 -51.9%
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 NNBR vs SEB — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NNBR vs SEB — Which Stock Is More Undervalued?

SEB 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 NN, Inc. (NNBR) and Seaboard Corporation (SEB) 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.

NNBR currently trades at $3.08 with a QOC of 5.4/10, while SEB trades at $5047.22 with a QOC of 8.6/10.

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).