OUST vs PLXS

Ouster, Inc. vs Plexus Corp. — Valuation Comparison 2026

OUST

Electronic Components
Ouster, Inc.
Quality
6.6
out of 10
Value Trap
24
SAFE
Price
$42.33
Last close
Models
11/13
Active
VS

PLXS

Electronic Components
Plexus Corp.
Quality
9.2
out of 10
Value Trap
6
SAFE
Price
$267.89
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType OUST Fair ValueOUST Upside PLXS Fair ValuePLXS Upside
Bayesian DCF Intrinsic $13.18 -68.9% $48.25 -82.0%
Earnings Power Value Intrinsic $4.94 -81.3% $74.01 -72.4%
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 $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

OUST vs PLXS — Which Stock Is More Undervalued?

PLXS scores higher with a 9.2/10 quality rating vs OUST's 6.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ouster, Inc. (OUST) and Plexus Corp. (PLXS) 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.

OUST currently trades at $42.33 with a QOC of 6.6/10, while PLXS trades at $267.89 with a QOC of 9.2/10.

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).