FBIO vs FBLG

Fortress Biotech, Inc. vs FibroBiologics, Inc. — Valuation Comparison 2026

FBIO

Biotechnology
Fortress Biotech, Inc.
Quality
5.9
out of 10
Value Trap
18
SAFE
Price
$2.81
Last close
Models
13/13
Active
VS

FBLG

Biotechnology
FibroBiologics, Inc.
Quality
4.1
out of 10
Value Trap
6
SAFE
Price
$1.13
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType FBIO Fair ValueFBIO Upside FBLG Fair ValueFBLG Upside
Bayesian DCF Intrinsic $4.87 +73.4% $0.24 -78.6%
Earnings Power Value Intrinsic $3.96 +70.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $4.01 +42.5% $1.70 +50.3%
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 FBIO vs FBLG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FBIO vs FBLG — Which Stock Is More Undervalued?

FBIO scores higher with a 5.9/10 quality rating vs FBLG's 4.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Fortress Biotech, Inc. (FBIO) and FibroBiologics, Inc. (FBLG) 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.

FBIO currently trades at $2.81 with a QOC of 5.9/10, while FBLG trades at $1.13 with a QOC of 4.1/10.

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).