SNES vs UAN

SenesTech, Inc. vs CVR Partners, LP — Valuation Comparison 2026

SNES

Agricultural Chemicals
SenesTech, Inc.
Quality
6.5
out of 10
Value Trap
21
SAFE
Price
$1.73
Last close
Models
11/13
Active
VS

UAN

Agricultural Chemicals
CVR Partners, LP
Quality
9.0
out of 10
Value Trap
18
SAFE
Price
$121.30
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType SNES Fair ValueSNES Upside UAN Fair ValueUAN Upside
Bayesian DCF Intrinsic $0.97 -43.8% $171.38 +41.3%
Earnings Power Value Intrinsic $2.17 +39.9% $84.38 -30.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 SNES vs UAN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SNES vs UAN — Which Stock Is More Undervalued?

UAN scores higher with a 9.0/10 quality rating vs SNES's 6.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing SenesTech, Inc. (SNES) and CVR Partners, LP (UAN) 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.

SNES currently trades at $1.73 with a QOC of 6.5/10, while UAN trades at $121.30 with a QOC of 9.0/10.

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).