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Haffner Energy S.A (ALHAF) Fair Value & Analysis

Utilities · FR · Market cap €43.5M

Price€0.2455
Fair Value€0.1600
Upside-34.8%
Quality95/100
Evidence: Low Range €0.1200 – €0.2000

Fair value as of: Jun 24, 2026

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Analysis

Haffner Energy S.A (ALHAF) currently trades at €0.2455, while our model-based Fair Value estimate is €0.1600 — implying the stock looks roughly 34.8% overvalued today. We read business quality at 95/100 (high quality), in the Utilities 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: low).

About the company

Haffner Energy S.A. designs and builds HYNOCA, a carbon-negative solution for producing renewable hydrogen in France. The company focuses on energy engineering and biomass to energy projects. The company was founded in 1993 and is headquartered in Vitry-le-François, France.

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Frequently asked questions

Is Haffner Energy S.A (ALHAF) undervalued?
As of Jun 24, 2026, our model estimates a fair value of €0.1600 versus a price of €0.2455 — about −35% (overvalued). Model-based estimate, not financial advice.
What is the fair value of ALHAF?
Our 21-model fair value for Haffner Energy S.A is €0.1600 (as of Jun 24, 2026), built from audited fundamentals. The current price is €0.2455.
What is the quality score of ALHAF?
Haffner Energy S.A has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.