NEXN vs SDM

Nexxen International Ltd. vs Smart Digital Group Limited — Valuation Comparison 2026

NEXN

Advertising Agencies
Nexxen International Ltd.
Quality
2.1
out of 10
Value Trap
Price
$8.46
Last close
Models
12/13
Active
VS

SDM

Advertising Agencies
Smart Digital Group Limited
Quality
2.0
out of 10
Value Trap
Price
$1.85
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NEXN Fair ValueNEXN Upside SDM Fair ValueSDM Upside
Bayesian DCF Intrinsic $1.70 -79.9% $0.48 -73.9%
Earnings Power Value Intrinsic $17.29 +126.6% $0.36 -80.4%
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 NEXN vs SDM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NEXN vs SDM — Which Stock Is More Undervalued?

NEXN scores higher with a 2.1/10 quality rating vs SDM's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Nexxen International Ltd. (NEXN) and Smart Digital Group Limited (SDM) 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.

NEXN currently trades at $8.46 with a QOC of 2.1/10, while SDM trades at $1.85 with a QOC of 2.0/10.

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).