RAL vs REFR

Ralliant Corporation vs Research Frontiers Incorporated — Valuation Comparison 2026

RAL

Electronic Components
Ralliant Corporation
Quality
7.4
out of 10
Value Trap
Price
$62.34
Last close
Models
12/13
Active
VS

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

Model-by-Model Comparison

ModelType RAL Fair ValueRAL Upside REFR Fair ValueREFR Upside
Bayesian DCF Intrinsic $45.02 -27.8% $0.21 -72.2%
Earnings Power Value Intrinsic $151.93 +143.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.18 -76.1%
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 RAL vs REFR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RAL vs REFR — Which Stock Is More Undervalued?

RAL scores higher with a 7.4/10 quality rating vs REFR's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ralliant Corporation (RAL) and Research Frontiers Incorporated (REFR) 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.

RAL currently trades at $62.34 with a QOC of 7.4/10, while REFR trades at $0.76 with a QOC of 5.7/10.

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).