REFR vs SANM

Research Frontiers Incorporated vs Sanmina Corporation — Valuation Comparison 2026

REFR

Electronic Components
Research Frontiers Incorporated
Quality
5.7
out of 10
Value Trap
24
SAFE
Price
$0.76
Last close
Models
8/13
Active
VS

SANM

Electronic Components
Sanmina Corporation
Quality
7.9
out of 10
Value Trap
12
SAFE
Price
$263.23
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType REFR Fair ValueREFR Upside SANM Fair ValueSANM Upside
Bayesian DCF Intrinsic $0.21 -72.2% $144.69 -45.0%
Earnings Power Value Intrinsic $41.71 -84.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.18 -76.1% $78.26 -70.3%
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 REFR vs SANM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

REFR vs SANM — Which Stock Is More Undervalued?

SANM scores higher with a 7.9/10 quality rating vs REFR's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Research Frontiers Incorporated (REFR) and Sanmina Corporation (SANM) 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.

REFR currently trades at $0.76 with a QOC of 5.7/10, while SANM trades at $263.23 with a QOC of 7.9/10.

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).