LNTH vs NEOG

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

LNTH

In Vitro & In Vivo Diagnostic Substances
Lantheus Holdings, Inc.
Quality
10.0
out of 10
Value Trap
31
LOW
Price
$99.30
Last close
Models
13/13
Active
VS

NEOG

In Vitro & In Vivo Diagnostic Substances
Neogen Corporation
Quality
7.6
out of 10
Value Trap
39
LOW
Price
$8.97
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType LNTH Fair ValueLNTH Upside NEOG Fair ValueNEOG Upside
Bayesian DCF Intrinsic $66.86 -32.7% $0.94 -89.5%
Earnings Power Value Intrinsic $50.63 -49.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $129.46 +30.4% $14.98 +67.1%
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 NEOG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LNTH vs NEOG — Which Stock Is More Undervalued?

LNTH scores higher with a 10.0/10 quality rating vs NEOG's 7.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Lantheus Holdings, Inc. (LNTH) and Neogen Corporation (NEOG) 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 $99.30 with a QOC of 10.0/10, while NEOG trades at $8.97 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).