FAMI vs SJM

Farmmi, Inc. Ordinary Shares vs The J.M. Smucker Company — Valuation Comparison 2026

FAMI

Canned, Fruits, Veg, Preserves, Jams & Jellies
Farmmi, Inc. Ordinary Shares
Quality
2.0
out of 10
Value Trap
Price
$1.35
Last close
Models
9/13
Active
VS

SJM

Canned, Fruits, Veg, Preserves, Jams & Jellies
The J.M. Smucker Company
Quality
6.1
out of 10
Value Trap
8
SAFE
Price
$103.20
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType FAMI Fair ValueFAMI Upside SJM Fair ValueSJM Upside
Bayesian DCF Intrinsic $0.67 -50.2% $52.79 -48.8%
Earnings Power Value Intrinsic $0.01 -98.8% $117.01 +13.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 FAMI vs SJM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FAMI vs SJM — Which Stock Is More Undervalued?

SJM scores higher with a 6.1/10 quality rating vs FAMI's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Farmmi, Inc. Ordinary Shares (FAMI) and The J.M. Smucker Company (SJM) 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.

FAMI currently trades at $1.35 with a QOC of 2.0/10, while SJM trades at $103.20 with a QOC of 6.1/10.

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).