NMAX vs SBGI

Newsmax, Inc. vs Sinclair, Inc. — Valuation Comparison 2026

NMAX

Broadcasting
Newsmax, Inc.
Quality
5.3
out of 10
Value Trap
Price
$8.74
Last close
Models
12/13
Active
VS

SBGI

Broadcasting
Sinclair, Inc.
Quality
6.5
out of 10
Value Trap
20
SAFE
Price
$14.30
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType NMAX Fair ValueNMAX Upside SBGI Fair ValueSBGI Upside
Bayesian DCF Intrinsic $2.00 -77.1% $22.03 +54.1%
Earnings Power Value Intrinsic $1.77 -72.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $1.73 -76.6% $5.71 -58.0%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for NMAX vs SBGI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NMAX vs SBGI — Which Stock Is More Undervalued?

SBGI scores higher with a 6.5/10 quality rating vs NMAX's 5.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Newsmax, Inc. (NMAX) and Sinclair, Inc. (SBGI) 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.

NMAX currently trades at $8.74 with a QOC of 5.3/10, while SBGI trades at $14.30 with a QOC of 6.5/10.

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).