CGON vs CGTX

CG Oncology, Inc. vs Cognition Therapeutics, Inc. — Valuation Comparison 2026

CGON

Biotechnology
CG Oncology, Inc.
Quality
5.0
out of 10
Value Trap
12
SAFE
Price
$60.73
Last close
Models
12/13
Active
VS

CGTX

Biotechnology
Cognition Therapeutics, Inc.
Quality
4.1
out of 10
Value Trap
36
LOW
Price
$1.19
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType CGON Fair ValueCGON Upside CGTX Fair ValueCGTX Upside
Bayesian DCF Intrinsic $17.93 -70.5% $0.51 -57.3%
Earnings Power Value Intrinsic $29.69 -56.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $7.48 -87.7% $0.64 -45.8%
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 CGON vs CGTX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CGON vs CGTX — Which Stock Is More Undervalued?

CGON scores higher with a 5.0/10 quality rating vs CGTX's 4.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing CG Oncology, Inc. (CGON) and Cognition Therapeutics, Inc. (CGTX) 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.

CGON currently trades at $60.73 with a QOC of 5.0/10, while CGTX trades at $1.19 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).