LNTH vs LQDA

Lantheus Holdings, Inc. vs Liquidia Corporation — Valuation Comparison 2026

LNTH

Drug Manufacturers - Specialty & Generic
Lantheus Holdings, Inc.
Quality
10.0
out of 10
Value Trap
31
LOW
Price
$99.62
Last close
Models
13/13
Active
VS

LQDA

Drug Manufacturers - Specialty & Generic
Liquidia Corporation
Quality
6.7
out of 10
Value Trap
24
SAFE
Price
$62.03
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType LNTH Fair ValueLNTH Upside LQDA Fair ValueLQDA Upside
Bayesian DCF Intrinsic $66.67 -33.1% $2.28 -96.3%
Earnings Power Value Intrinsic $50.63 -49.2% $5.66 -90.9%
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 LNTH vs LQDA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LNTH vs LQDA — Which Stock Is More Undervalued?

LNTH scores higher with a 10.0/10 quality rating vs LQDA's 6.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Lantheus Holdings, Inc. (LNTH) and Liquidia Corporation (LQDA) 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.

LNTH currently trades at $99.62 with a QOC of 10.0/10, while LQDA trades at $62.03 with a QOC of 6.7/10.

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).