FBRX vs FTRE

Forte Biosciences, Inc. vs Fortrea Holdings Inc. — Valuation Comparison 2026

FBRX

Biotechnology
Forte Biosciences, Inc.
Quality
4.1
out of 10
Value Trap
18
SAFE
Price
$20.77
Last close
Models
9/13
Active
VS

FTRE

Biotechnology
Fortrea Holdings Inc.
Quality
5.3
out of 10
Value Trap
27
LOW
Price
$15.00
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType FBRX Fair ValueFBRX Upside FTRE Fair ValueFTRE Upside
Bayesian DCF Intrinsic $7.11 -65.8% $0.17 -98.8%
Earnings Power Value Intrinsic $6.28 -77.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $3.39 -83.7% $12.15 -19.0%
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 FBRX vs FTRE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FBRX vs FTRE — Which Stock Is More Undervalued?

FTRE scores higher with a 5.3/10 quality rating vs FBRX's 4.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Forte Biosciences, Inc. (FBRX) and Fortrea Holdings Inc. (FTRE) 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.

FBRX currently trades at $20.77 with a QOC of 4.1/10, while FTRE trades at $15.00 with a QOC of 5.3/10.

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).