AlphaStocks

CVNA vs PAG

As of Sep 20, 2026, Carvana (CVNA) scores 2.5/10 and Penske Automotive Group (PAG) 8.2/10 on AlphaStocks' five-model composite, so PAG scores higher.

CVNA

CARVANA CO.

2.5

Avoid

$65.10

PAG

PENSKE AUTOMOTIVE GROUP, INC.

8.2

Strong Buy

$212.54

CVNA vs PAG: Which is the Better Investment?

CARVANA CO. (CVNA) scores 2.5/10 while PENSKE AUTOMOTIVE GROUP, INC. (PAG) scores 8.2/10 on AlphaStocks' composite model. PENSKE AUTOMOTIVE GROUP, INC. has the higher composite rating of Strong Buy. On a P/E basis, CARVANA CO. trades at 10.4x, making it the more attractively priced of the two.

This comparison is algorithmically generated and is not financial advice.

MetricCVNAPAG
Scores & Fundamentals
Composite Score2.5/108.2/10
RatingAvoidStrong Buy
Price$65.10$212.54
P/E Ratio10.415.0
ROE49.2%16.6%
Market Cap$10B$14B
Fair Value
Dividend Yield2.7%
Sector Rank#180 of 194#2 of 194
Model Verdicts
PiotroskiAttractiveStrong
BuffettNeutralStrong
GrahamCautionNeutral
LynchLimited DataAttractive
GreenblattAttractiveStrong
View full CVNAanalysis →View full PAGanalysis →

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CVNA vs PAG: Which Stock Scores Higher?

CARVANA CO. (CVNA) and PENSKE AUTOMOTIVE GROUP, INC. (PAG) are both in the Consumer Discretionary sector. PAG currently leads with a composite score of 8.2/10 (Strong Buy) compared to CVNA's 2.5/10 (Avoid).

The AlphaStocks composite score evaluates each stock across four dimensions: Quality (business strength measured by Piotroski F-Score and Buffett quality criteria), Value (discount to intrinsic worth using Graham, Lynch, and Greenblatt models), Momentum (6-month price trend), and Timing (a confirmation signal that requires both value and momentum to align). A higher composite score indicates stronger overall fundamentals combined with favorable market conditions.

This comparison uses the same scoring framework for both companies, ensuring an apples-to-apples evaluation. Scores are recalculated daily after market close using data from SEC filings and market prices. Read the full methodology to understand how each model contributes to the composite score.

Scores are algorithm-generated research tools, not investment recommendations. Past performance does not guarantee future results. Always do your own due diligence. Full disclaimer