VEON vs VOD

VEON Ltd. vs Vodafone Group Plc — Valuation Comparison 2026

VEON

Radiotelephone Communications
VEON Ltd.
Quality
2.1
out of 10
Value Trap
Price
$56.14
Last close
Models
11/13
Active
VS

VOD

Radiotelephone Communications
Vodafone Group Plc
Quality
1.7
out of 10
Value Trap
Price
$14.96
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType VEON Fair ValueVEON Upside VOD Fair ValueVOD Upside
Bayesian DCF Intrinsic $16.70 -70.3% $5.44 -63.6%
Earnings Power Value Intrinsic $124.30 +153.0% $6.72 -57.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-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 VEON vs VOD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

VEON vs VOD — Which Stock Is More Undervalued?

VEON scores higher with a 2.1/10 quality rating vs VOD's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing VEON Ltd. (VEON) and Vodafone Group Plc (VOD) 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.

VEON currently trades at $56.14 with a QOC of 2.1/10, while VOD trades at $14.96 with a QOC of 1.7/10.

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).