Ieva Group (ALIEV) Fair Value & Analysis
Consumer Defensive · FR · Market cap €76.2M
Fair value as of: Jun 24, 2026
Analysis
Ieva Group (ALIEV) currently trades at €7.80, while our model-based Fair Value estimate is €2.83 — implying the stock looks roughly 63.7% overvalued today. We read business quality at 81/100 (high quality), in the Consumer Defensive 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
Ieva Group S.A. operates as a beauty-tech company that develops an ecosystem of brands, services, and personalized beauty and wellness services in France, Europe, and Asia. The company operates in three business units, IEVA Tech, IEVA Beauty, and IEVA Experience. The company's IEVA Tech division designs, develops, and industrializes proprietary technologies for measuring and analyzing skin, hair and the environment, as well as the software engines and AI algorithms that offer recommendations for care, services, and content. Its products include point-of-sale diagnostic devices, such as IOMA Sphere diagnostic device, IOMA In-Lab, Twin.C connected jewel, and i-diag. its IEVA Beauty division offers skincare products through its brands, IOMA, IOMA Hair, Made with CARE and Elenature, as well as the makeup and eye care products through the brands L'Atelier du Sourcil and Boudoir du Regard. Its products include moisturizers, serums, eye contour care, cleansing and skin preparation products…
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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.