PRFX vs RMTI

PRF Technologies Ltd. vs Rockwell Medical, Inc. — Valuation Comparison 2026

PRFX

Drug Manufacturers - Specialty & Generic
PRF Technologies Ltd.
Quality
4.4
out of 10
Value Trap
39
LOW
Price
$1.37
Last close
Models
8/13
Active
VS

RMTI

Drug Manufacturers - Specialty & Generic
Rockwell Medical, Inc.
Quality
6.0
out of 10
Value Trap
12
SAFE
Price
$0.76
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType PRFX Fair ValuePRFX Upside RMTI Fair ValueRMTI Upside
Bayesian DCF Intrinsic $2.07 +50.8% $0.22 -71.5%
Earnings Power Value Intrinsic $0.19 -77.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $2.26 +65.3% $0.71 -6.2%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PRFX vs RMTI — Which Stock Is More Undervalued?

RMTI scores higher with a 6.0/10 quality rating vs PRFX's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing PRF Technologies Ltd. (PRFX) and Rockwell Medical, Inc. (RMTI) 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.

PRFX currently trades at $1.37 with a QOC of 4.4/10, while RMTI trades at $0.76 with a QOC of 6.0/10.

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).