RAYA vs RFIL

Erayak Power Solution Group Inc vs RF Industries, Ltd. — Valuation Comparison 2026

RAYA

Electrical Equipment & Parts
Erayak Power Solution Group Inc
Quality
1.4
out of 10
Value Trap
12
SAFE
Price
$3.01
Last close
Models
7/13
Active
VS

RFIL

Electrical Equipment & Parts
RF Industries, Ltd.
Quality
8.3
out of 10
Value Trap
11
SAFE
Price
$18.52
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType RAYA Fair ValueRAYA Upside RFIL Fair ValueRFIL Upside
Bayesian DCF Intrinsic $0.18 -96.1% $0.56 -97.0%
Earnings Power Value Intrinsic $2.24 -87.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $6.28 +36.5% $8.36 -54.8%
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 RAYA vs RFIL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RAYA vs RFIL — Which Stock Is More Undervalued?

RFIL scores higher with a 8.3/10 quality rating vs RAYA's 1.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Erayak Power Solution Group Inc (RAYA) and RF Industries, Ltd. (RFIL) 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.

RAYA currently trades at $3.01 with a QOC of 1.4/10, while RFIL trades at $18.52 with a QOC of 8.3/10.

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).