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Genomma Lab Internacional, S.A. (GNMLF) Fair Value & Analysis

Healthcare · US · Market cap $821M

Price$0.8368
Fair Value$1.93
Upside+130.6%
Quality95/100
Evidence: High Range $0.9800 – $2.57

Analysis

Genomma Lab Internacional, S.A. (GNMLF) currently trades at $0.8368, while our model-based Fair Value estimate is $1.93 — implying the stock looks roughly 130.6% undervalued today. We read business quality at 95/100 (high quality), in the Healthcare 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

Genomma Lab Internacional, S.A.B. de C.V., together with its subsidiaries, provides pharmaceutical and personal care products primarily in Mexico and internationally. It develops, sells, and markets a range of branded products in the categories of anti-acne products, self-diagnosis, antibacterial, cream to improve the texture of scarred skin, shampoos, dandruff shampoos, soaps, razors, products to prevent hair loss, ointment for muscle pain, anti-fungal treatments for the relief of colitis, products to counter stress levels, anti-wrinkles, antacids, anti-ulcers, anti-flu, protection and improvement products, treatments against hemorrhoids, against varicose veins, osteoarthritis, ophthalmic medications, infant nutrition, and others. Genomma Lab Internacional, S.A.B. de C.V. was incorporated in 1996 and is based in Mexico City, Mexico.

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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.