IRD vs IRWD

Opus Genetics, Inc. vs Ironwood Pharmaceuticals, 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

IRWD

Pharmaceutical Preparations
Ironwood Pharmaceuticals, Inc.
Quality
7.6
out of 10
Value Trap
39
LOW
Price
$3.57
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType IRD Fair ValueIRD Upside IRWD Fair ValueIRWD Upside
Bayesian DCF Intrinsic $1.14 -74.0% $15.29 +328.3%
Earnings Power Value Intrinsic $5.71 +59.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $4.95 +13.6% $5.31 +48.6%
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 IRD vs IRWD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IRD vs IRWD — Which Stock Is More Undervalued?

IRWD scores higher with a 7.6/10 quality rating vs IRD's 5.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Opus Genetics, Inc. (IRD) and Ironwood Pharmaceuticals, Inc. (IRWD) 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 IRWD trades at $3.57 with a QOC of 7.6/10.

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).