INSM vs INVA

Insmed Incorporated vs Innoviva, Inc. — Valuation Comparison 2026

INSM

Biotechnology
Insmed Incorporated
Quality
4.0
out of 10
Value Trap
18
SAFE
Price
$108.37
Last close
Models
11/13
Active
VS

INVA

Biotechnology
Innoviva, Inc.
Quality
9.1
out of 10
Value Trap
12
SAFE
Price
$21.89
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType INSM Fair ValueINSM Upside INVA Fair ValueINVA Upside
Bayesian DCF Intrinsic $36.85 -66.0% $28.03 +28.0%
Earnings Power Value Intrinsic $64.56 -52.2% $15.54 -29.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
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 INSM vs INVA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

INSM vs INVA — Which Stock Is More Undervalued?

INVA scores higher with a 9.1/10 quality rating vs INSM's 4.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Insmed Incorporated (INSM) and Innoviva, Inc. (INVA) 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.

INSM currently trades at $108.37 with a QOC of 4.0/10, while INVA trades at $21.89 with a QOC of 9.1/10.

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).