Orlen S.A (ORLNY) Fair Value & Analysis
Energy · US · Market cap $25.7B
Analysis
Orlen S.A (ORLNY) currently trades at $22.10, while our model-based Fair Value estimate is $22.75 — implying the stock looks roughly 2.9% undervalued today. We read business quality at 91/100 (high quality), in the Energy sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Orlen S.A. operates in refining, petrochemical, energy, retail, gas, and upstream business. It engages in the processing and wholesale of refinery products, such as crude oil; production and sale of fuel, oil, chemicals, and petrochemicals, as well as provision of supporting services; production, distribution, and sale of electricity and heat from conventional and renewable energy sources comprising solar photovoltaics, as well as natural gas; trading of electricity; exploration and extraction of mineral resources; exploration, production, and import of natural gas; and trading and storage of gas and liquid gas. The company is also involved in the exploration, recognition, and extraction of hydrocarbons; fuel station activities; and provision of transportation, maintenance and overhaul, laboratory, security, design, administrative, courier, insurance, and financial services, as well as press distribution, and media activities, such as newspapers and websites. It offers petrol, diese…
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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.