Neogen Corporation (NEOG) Fair Value & Analysis
Healthcare · US · Market cap $2.0B
Analysis
Neogen Corporation (NEOG) currently trades at $9.18, while our model-based Fair Value estimate is $5.09 — implying the stock looks roughly 44.6% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare 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
Neogen Corporation develops, manufactures, and markets various products and services for food and animal safety in the United States and internationally. It operates through two segments, Food Safety and Animal Safety. The Food Safety segment offers diagnostic test kits and complementary products to detect dangerous and unintended substances in human food and animal feed, including foodborne pathogens, spoilage organisms, natural toxins, food allergens, ruminant by-products, meat speciation, drug residues, pesticide residues, and general sanitation concerns, as well as food quality and nutritional components. It also provides software systems that help testers analyze and store results; natural toxin tests; rapid sanitation tests; culture and prepared media products; filter tests for microbial analysis of water; sample collection products; and food safety analysis laboratory services. This segment sells its products to the milling and grain, meat and poultry, ready-to-eat, fruits an…
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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.