OMC vs QMMM

Omnicom Group Inc. vs QMMM Holdings Limited — Valuation Comparison 2026

OMC

Advertising Agencies
Omnicom Group Inc.
Quality
6.7
out of 10
Value Trap
26
LOW
Price
$74.09
Last close
Models
13/13
Active
VS

QMMM

Advertising Agencies
QMMM Holdings Limited
Quality
4.4
out of 10
Value Trap
Price
$119.40
Last close
Models
5/13
Active

Model-by-Model Comparison

ModelType OMC Fair ValueOMC Upside QMMM Fair ValueQMMM Upside
Bayesian DCF Intrinsic $68.39 -7.7% $35.39 -70.4%
Earnings Power Value Intrinsic $19.88 -73.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $125.84 +69.8% $112.41 -5.9%
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 OMC vs QMMM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

OMC vs QMMM — Which Stock Is More Undervalued?

OMC scores higher with a 6.7/10 quality rating vs QMMM's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Omnicom Group Inc. (OMC) and QMMM Holdings Limited (QMMM) 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.

OMC currently trades at $74.09 with a QOC of 6.7/10, while QMMM trades at $119.40 with a QOC of 4.4/10.

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).