NIPG vs NWS

NIP Group Inc. vs News Corporation — Valuation Comparison 2026

NIPG

Entertainment
NIP Group Inc.
Quality
2.0
out of 10
Value Trap
Price
$0.42
Last close
Models
11/13
Active
VS

NWS

Entertainment
News Corporation
Quality
8.7
out of 10
Value Trap
8
SAFE
Price
$30.34
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType NIPG Fair ValueNIPG Upside NWS Fair ValueNWS Upside
Bayesian DCF Intrinsic $0.11 -73.5% $13.40 -55.8%
Earnings Power Value Intrinsic $1.34 +85.6% $0.75 -97.5%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

NIPG vs NWS — Which Stock Is More Undervalued?

NWS scores higher with a 8.7/10 quality rating vs NIPG's 2.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing NIP Group Inc. (NIPG) and News Corporation (NWS) 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.

NIPG currently trades at $0.42 with a QOC of 2.0/10, while NWS trades at $30.34 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).