Cheops Technology France Société anonyme (MLCHE) Fair Value & Analysis
Technology · FR · Market cap €131M
Fair value as of: Jun 24, 2026
Analysis
Cheops Technology France Société anonyme (MLCHE) currently trades at €57.00, while our model-based Fair Value estimate is €61.29 — implying the stock looks roughly 7.5% undervalued today. We read business quality at 89/100 (high quality), in the Technology 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: medium) — always confirm before acting.
About the company
Cheops Technology France Société anonyme provides cloud computing services. The company offers data management, digital workspace, IT transformation, and consulting solutions. It also provides networking, unified communications, and security services, as well as offers hyper com, a tool for business communication. In addition, the company provides hyper wifi, a wireless network infrastructure; and Hyper Tom to provide users with a single and unified communications tool for everyday use. Further, it provides systems migration, cloud and other modern applications, and research, and development tools. Additionally, the company offers cloud and managed services, which includes healthcare data hosting and manages services, as well as provides hyper x range, a service appliance; and iCod, a managed cloud for sensitive applications. Cheops Technology France Société anonyme was incorporated in 1998 and is headquartered in Bordeaux, France.
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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.