Minth Group (MNTHF) Fair Value & Analysis
Consumer Cyclical · US · Market cap $5.1B
Analysis
Minth Group (MNTHF) currently trades at $4.35, while our model-based Fair Value estimate is $3.78 — implying the stock looks roughly 13.1% overvalued today. We read business quality at 92/100 (high quality), in the Consumer Cyclical 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: high).
About the company
Minth Group Limited, an investment holding company, designs, develops, manufactures, processes, and sells automobile body parts and moulds of passenger cars. The company manufactures, develops, and sells motor systems for electric vehicles, as well as metal molds, automobile body parts; automobile body parts manufacturing equipment, automation machines, software, production lines, and automobile drives; and offers bookkeeping services. It also engages in the import and export trading, wholesale of packaging materials, logistics, and technology import activities; manufacture and sale of electrical and electronic equipment; production, operation trading, and import and export trade of deformed aluminum alloys, aluminum profiles, and aluminum extrusion moulds; and research, design, development, sales, import, and export of robots. In addition, the company provides automobile glass, light metals casting, customer, research and market development, general business, business management, n…
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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.