NMAX vs NXST

Newsmax, Inc. vs Nexstar Media Group, 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

NXST

Broadcasting
Nexstar Media Group, Inc.
Quality
8.7
out of 10
Value Trap
17
SAFE
Price
$185.94
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType NMAX Fair ValueNMAX Upside NXST Fair ValueNXST Upside
Bayesian DCF Intrinsic $2.00 -77.1% $497.24 +167.4%
Earnings Power Value Intrinsic $1.77 -72.2% $41.96 -78.7%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

NMAX vs NXST — Which Stock Is More Undervalued?

NXST scores higher with a 8.7/10 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 Nexstar Media Group, Inc. (NXST) 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 NXST trades at $185.94 with a QOC of 8.7/10.

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).