IRD vs IRON

Opus Genetics, Inc. vs Disc Medicine, Inc. — Valuation Comparison 2026

IRD

Pharmaceutical Preparations
Opus Genetics, Inc.
Quality
5.2
out of 10
Value Trap
44
WARN
Price
$4.36
Last close
Models
9/13
Active
VS

IRON

Pharmaceutical Preparations
Disc Medicine, Inc.
Quality
4.2
out of 10
Value Trap
12
SAFE
Price
$69.57
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType IRD Fair ValueIRD Upside IRON Fair ValueIRON Upside
Bayesian DCF Intrinsic $1.14 -74.0% $20.84 -70.0%
Earnings Power Value Intrinsic $32.12 -53.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $4.95 +13.6% $63.28 -9.0%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

IRD vs IRON — Which Stock Is More Undervalued?

IRD scores higher with a 5.2/10 quality rating vs IRON's 4.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Opus Genetics, Inc. (IRD) and Disc Medicine, Inc. (IRON) 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.

IRD currently trades at $4.36 with a QOC of 5.2/10, while IRON trades at $69.57 with a QOC of 4.2/10.

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).