SMPL vs UCFI

The Simply Good Foods Company vs CN Healthy Food Tech Group Corp — Valuation Comparison 2026

SMPL

Food and Kindred Products
The Simply Good Foods Company
Quality
9.4
out of 10
Value Trap
12
SAFE
Price
$11.52
Last close
Models
13/13
Active
VS

UCFI

Food and Kindred Products
CN Healthy Food Tech Group Corp
Quality
5.7
out of 10
Value Trap
Price
$5.51
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType SMPL Fair ValueSMPL Upside UCFI Fair ValueUCFI Upside
Bayesian DCF Intrinsic $24.41 +111.9% $1.16 -78.9%
Earnings Power Value Intrinsic $14.82 +28.6% $1.40 -74.7%
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 SMPL vs UCFI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SMPL vs UCFI — Which Stock Is More Undervalued?

SMPL scores higher with a 9.4/10 quality rating vs UCFI's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing The Simply Good Foods Company (SMPL) and CN Healthy Food Tech Group Corp (UCFI) 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.

SMPL currently trades at $11.52 with a QOC of 9.4/10, while UCFI trades at $5.51 with a QOC of 5.7/10.

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).