CALM vs MGN

Cal-Maine Foods, Inc. vs Megan Holdings Limited — Valuation Comparison 2026

CALM

Agricultural Prod-Livestock & Animal Specialties
Cal-Maine Foods, Inc.
Quality
10.0
out of 10
Value Trap
16
SAFE
Price
$74.72
Last close
Models
12/13
Active
VS

MGN

Agricultural Prod-Livestock & Animal Specialties
Megan Holdings Limited
Quality
5.8
out of 10
Value Trap
Price
$0.15
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CALM Fair ValueCALM Upside MGN Fair ValueMGN Upside
Bayesian DCF Intrinsic $149.67 +100.3% $0.03 -79.3%
Earnings Power Value Intrinsic $75.15 +0.6% $0.02 -85.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 CALM vs MGN — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CALM vs MGN — Which Stock Is More Undervalued?

CALM scores higher with a 10.0/10 quality rating vs MGN's 5.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Cal-Maine Foods, Inc. (CALM) and Megan Holdings Limited (MGN) 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.

CALM currently trades at $74.72 with a QOC of 10.0/10, while MGN trades at $0.15 with a QOC of 5.8/10.

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).