CHSCM vs LMNR

CHS Inc vs Limoneira Co — Valuation Comparison 2026

CHSCM

Farm Products
CHS Inc
Quality
6.5
out of 10
Value Trap
6
SAFE
Price
$25.12
Last close
Models
13/13
Active
VS

LMNR

Farm Products
Limoneira Co
Quality
6.0
out of 10
Value Trap
24
SAFE
Price
$12.84
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType CHSCM Fair ValueCHSCM Upside LMNR Fair ValueLMNR Upside
Bayesian DCF Intrinsic $5.58 -77.8% $0.84 -93.5%
Earnings Power Value Intrinsic $1.42 -94.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $22.76 -9.4% $2.78 -78.4%
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 CHSCM vs LMNR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CHSCM vs LMNR — Which Stock Is More Undervalued?

CHSCM scores higher with a 6.5/10 quality rating vs LMNR's 6.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing CHS Inc (CHSCM) and Limoneira Co (LMNR) 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.

CHSCM currently trades at $25.12 with a QOC of 6.5/10, while LMNR trades at $12.84 with a QOC of 6.0/10.

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).