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DLSI (ALDLS) Fair Value & Analysis

Industrials · FR · Market cap €27.3M

Price€10.30
Fair Value€19.55
Upside+89.8%
Quality94/100
Evidence: High Range €15.82 – €23.28

Fair value as of: Jun 25, 2026

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Analysis

DLSI (ALDLS) currently trades at €10.30, while our model-based Fair Value estimate is €19.55 — implying the stock looks roughly 89.8% undervalued today. We read business quality at 94/100 (high quality), in the Industrials 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

DLSI provides employment solutions in France, Germany, Switzerland, Luxembourg, and rest of the European Union. The company operates a network of employment agencies for the wood and tertiary industries. It offers its services under the DLSI, PEMSA GROUP, EMO DLSI, and TERCIO brands. DLSI was incorporated in 1992 and is headquartered in Forbach, France.

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Frequently asked questions

Is DLSI (ALDLS) undervalued?
As of Jun 25, 2026, our model estimates a fair value of €19.55 versus a price of €10.30 — about +90% (undervalued). Model-based estimate, not financial advice.
What is the fair value of ALDLS?
Our 21-model fair value for DLSI is €19.55 (as of Jun 25, 2026), built from audited fundamentals. The current price is €10.30.
What is the quality score of ALDLS?
DLSI has a Quality Score of 94/100, 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.