TRVI vs TXMD

Trevi Therapeutics, Inc. vs TherapeuticsMD, Inc. — Valuation Comparison 2026

TRVI

Pharmaceutical Preparations
Trevi Therapeutics, Inc.
Quality
4.2
out of 10
Value Trap
30
LOW
Price
$14.14
Last close
Models
7/13
Active
VS

TXMD

Pharmaceutical Preparations
TherapeuticsMD, Inc.
Quality
6.5
out of 10
Value Trap
32
LOW
Price
$2.14
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType TRVI Fair ValueTRVI Upside TXMD Fair ValueTXMD Upside
Bayesian DCF Intrinsic $3.83 -72.9% $4.61 +115.3%
Earnings Power Value Intrinsic $0.75 -63.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.36 -97.4% $1.98 -6.0%
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 TRVI vs TXMD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

TRVI vs TXMD — Which Stock Is More Undervalued?

TXMD scores higher with a 6.5/10 quality rating vs TRVI's 4.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Trevi Therapeutics, Inc. (TRVI) and TherapeuticsMD, Inc. (TXMD) 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.

TRVI currently trades at $14.14 with a QOC of 4.2/10, while TXMD trades at $2.14 with a QOC of 6.5/10.

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).