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Photonike Capital SA (MLPHO) Fair Value & Analysis

Financial Services · FR · Market cap €12.0M

Price€0.1080
Fair Value€0.2160
Upside+100.0%
Quality83/100
Evidence: Low Range €0.1650 – €0.3240

Fair value as of: Jun 26, 2026

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Analysis

Photonike Capital SA (MLPHO) currently trades at €0.1080, while our model-based Fair Value estimate is €0.2160 — implying the stock looks roughly 100.0% undervalued today. We read business quality at 83/100 (high quality), in the Financial Services 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: low) — always confirm before acting.

About the company

Photonike Capital SA provides bespoke risk protection solutions in the industrial and financial processes. It offers tailor made solutions for credit enhancement, market hedging, and alternative finance purposes. The company was founded in 2008 and is based in Brussels, Belgium.

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Frequently asked questions

Is Photonike Capital SA (MLPHO) undervalued?
As of Jun 26, 2026, our model estimates a fair value of €0.2160 versus a price of €0.1080 — about +100% (undervalued). Model-based estimate, not financial advice.
What is the fair value of MLPHO?
Our 21-model fair value for Photonike Capital SA is €0.2160 (as of Jun 26, 2026), built from audited fundamentals. The current price is €0.1080.
What is the quality score of MLPHO?
Photonike Capital SA has a Quality Score of 83/100, 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.