RAYA vs SKYX

Erayak Power Solution Group Inc vs SKYX Platforms Corp. — 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

SKYX

Electrical Equipment & Parts
SKYX Platforms Corp.
Quality
5.8
out of 10
Value Trap
43
WARN
Price
$1.12
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType RAYA Fair ValueRAYA Upside SKYX Fair ValueSKYX Upside
Bayesian DCF Intrinsic $0.18 -96.1% $0.32 -71.0%
Earnings Power Value Intrinsic $0.58 -45.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $6.28 +36.5% $0.19 -82.8%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 SKYX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RAYA vs SKYX — Which Stock Is More Undervalued?

SKYX scores higher with a 5.8/10 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 SKYX Platforms Corp. (SKYX) 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 SKYX trades at $1.12 with a QOC of 5.8/10.

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).