RMTI vs RNAZ

Rockwell Medical, Inc. vs TransCode Therapeutics, Inc. — Valuation Comparison 2026

RMTI

Pharmaceutical Preparations
Rockwell Medical, Inc.
Quality
6.0
out of 10
Value Trap
12
SAFE
Price
$0.74
Last close
Models
11/13
Active
VS

RNAZ

Pharmaceutical Preparations
TransCode Therapeutics, Inc.
Quality
3.9
out of 10
Value Trap
29
LOW
Price
$5.14
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType RMTI Fair ValueRMTI Upside RNAZ Fair ValueRNAZ Upside
Bayesian DCF Intrinsic $0.22 -69.8% $9.29 +80.8%
Earnings Power Value Intrinsic $0.19 -77.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $1.05 +40.7% $18.92 +268.1%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

RMTI vs RNAZ — Which Stock Is More Undervalued?

RMTI scores higher with a 6.0/10 quality rating vs RNAZ's 3.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Rockwell Medical, Inc. (RMTI) and TransCode Therapeutics, Inc. (RNAZ) 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.

RMTI currently trades at $0.74 with a QOC of 6.0/10, while RNAZ trades at $5.14 with a QOC of 3.9/10.

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).