EVC vs MGNI

Entravision Communications Corp vs Magnite, Inc. — Valuation Comparison 2026

EVC

Advertising Agencies
Entravision Communications Corp
Quality
6.5
out of 10
Value Trap
24
SAFE
Price
$9.38
Last close
Models
12/13
Active
VS

MGNI

Advertising Agencies
Magnite, Inc.
Quality
8.0
out of 10
Value Trap
29
LOW
Price
$14.43
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType EVC Fair ValueEVC Upside MGNI Fair ValueMGNI Upside
Bayesian DCF Intrinsic $3.05 -67.4% $13.57 -6.0%
Earnings Power Value Intrinsic $4.53 -51.7% $6.41 -55.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 EVC vs MGNI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EVC vs MGNI — Which Stock Is More Undervalued?

MGNI scores higher with a 8.0/10 quality rating vs EVC's 6.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Entravision Communications Corp (EVC) and Magnite, Inc. (MGNI) 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.

EVC currently trades at $9.38 with a QOC of 6.5/10, while MGNI trades at $14.43 with a QOC of 8.0/10.

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).