LEGN vs LGVN

Legend Biotech Corporation vs Longeveron Inc. — Valuation Comparison 2026

LEGN

Pharmaceutical Preparations
Legend Biotech Corporation
Quality
1.7
out of 10
Value Trap
Price
$27.16
Last close
Models
12/13
Active
VS

LGVN

Pharmaceutical Preparations
Longeveron Inc.
Quality
5.8
out of 10
Value Trap
30
LOW
Price
$0.77
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType LEGN Fair ValueLEGN Upside LGVN Fair ValueLGVN Upside
Bayesian DCF Intrinsic $8.41 -69.1% $0.46 -40.7%
Earnings Power Value Intrinsic $10.13 -57.6% $0.39 -58.1%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

LEGN vs LGVN — Which Stock Is More Undervalued?

LGVN scores higher with a 5.8/10 quality rating vs LEGN's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Legend Biotech Corporation (LEGN) and Longeveron Inc. (LGVN) 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.

LEGN currently trades at $27.16 with a QOC of 1.7/10, while LGVN trades at $0.77 with a QOC of 5.8/10.

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).