FMX vs MNST

Fomento Economico Mexicano S.A. vs Monster Beverage Corporation — Valuation Comparison 2026

FMX

Bottled & Canned Soft Drinks & Carbonated Waters
Fomento Economico Mexicano S.A.
Quality
6.9
out of 10
Value Trap
24
SAFE
Price
$120.86
Last close
Models
13/13
Active
VS

MNST

Bottled & Canned Soft Drinks & Carbonated Waters
Monster Beverage Corporation
Quality
9.6
out of 10
Value Trap
6
SAFE
Price
$87.99
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType FMX Fair ValueFMX Upside MNST Fair ValueMNST Upside
Bayesian DCF Intrinsic $25.38 -79.0% $23.26 -73.6%
Earnings Power Value Intrinsic $60.38 -50.0% $20.92 -76.2%
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 $•••.•• ••.•% $•••.•• ••.•%
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FMX vs MNST — Which Stock Is More Undervalued?

MNST scores higher with a 9.6/10 quality rating vs FMX's 6.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Fomento Economico Mexicano S.A. (FMX) and Monster Beverage Corporation (MNST) 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.

FMX currently trades at $120.86 with a QOC of 6.9/10, while MNST trades at $87.99 with a QOC of 9.6/10.

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).