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Ampol Limited (CTXAF) Fair Value & Analysis

Energy · US · Market cap $4.6B

Price$16.30
Fair Value$3.33
Upside-79.6%
Quality90/100
Evidence: High Range $2.50 – $4.16

Analysis

Ampol Limited (CTXAF) currently trades at $16.30, while our model-based Fair Value estimate is $3.33 — implying the stock looks roughly 79.6% overvalued today. We read business quality at 90/100 (high quality), in the Energy sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).

About the company

Ampol Limited purchases and sells petroleum products in Australia, New Zealand, Singapore, and the United States. It operates through Fuels and Infrastructure, Convenience Retail, and New Zealand segments. The Fuels and Infrastructure segment wholesales fuels and lubricants, including Lytton refinery, trading and shipping, distribution, infrastructure, and energy solutions. The Convenience Retail segment sells fuels through Ampol's network of stores. The New Zealand segment include Z Energy that distributes fuel in New Zealand market. It also engages in EV charging and home electricity solutions business. The company distributes its products through depots, terminals, pipelines, and service station sites. It sells fuels to various customers in defence, mining, transport, marine, agriculture, aviation, and other commercial and industrial sectors. The company was formerly known as Caltex Australia Limited and changed its name to Ampol Limited in May 2020. Ampol Limited was founded in …

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How we calculate Fair Value

Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.

Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.