Dyadic International, Inc (DYAI) Fair Value & Analysis
Healthcare · US · Market cap $28.5M
Fair value as of: Jun 24, 2026
Analysis
Dyadic International, Inc (DYAI) currently trades at $0.8060, while our model-based Fair Value estimate is $0.1500 — implying the stock looks roughly 81.4% 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
Dyadic International, Inc., together with its subsidiaries, a biotechnology platform company, develops, manufactures, and commercializes proteins and enzymes in the United States. The company offers Dapibus Protein Production Platform, a proprietary expression system designed to produce non-animal proteins and enzymes for non-pharmaceutical markets, including life sciences, food and nutrition, and bio-industrial sector; and C1 Protein Production Platform, a thermophilic fungal platform for large-scale production of proteins. It has a research and development agreement with VTT Technical Research Centre of Finland, Ltd; license agreement with Rubic One Health to develop various livestock vaccines; sub-license agreement with Phibro Animal Health/Abic Biological Laboratories to develop vaccines and treatments for livestock diseases; and collaboration with Scripps Research to evaluate monoclonal antibody and vaccine candidates targeting hantaviruses. Dyadic International, Inc. was found…
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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.