AXSM vs BBOT

Axsome Therapeutics, Inc. vs BridgeBio Oncology Therapeutics — Valuation Comparison 2026

AXSM

Biotechnology
Axsome Therapeutics, Inc.
Quality
6.1
out of 10
Value Trap
6
SAFE
Price
$232.83
Last close
Models
12/13
Active
VS

BBOT

Biotechnology
BridgeBio Oncology Therapeutics
Quality
6.2
out of 10
Value Trap
Price
$8.84
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType AXSM Fair ValueAXSM Upside BBOT Fair ValueBBOT Upside
Bayesian DCF Intrinsic $79.80 -65.7% $2.70 -69.5%
Earnings Power Value Intrinsic $82.92 -55.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $3.06 -98.7% $3.92 -55.6%
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 AXSM vs BBOT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AXSM vs BBOT — Which Stock Is More Undervalued?

BBOT scores higher with a 6.2/10 quality rating vs AXSM's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Axsome Therapeutics, Inc. (AXSM) and BridgeBio Oncology Therapeutics (BBOT) 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.

AXSM currently trades at $232.83 with a QOC of 6.1/10, while BBOT trades at $8.84 with a QOC of 6.2/10.

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).