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Linhai Co (600099) Fair Value & Analysis

Consumer Cyclical · CN · Market cap 2.0B CNY

Price¥8.64
Fair Value¥1.90
Upside-78.0%
Quality95/100
Evidence: High Range ¥1.47 – ¥2.38

Fair value as of: Jun 24, 2026

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Analysis

Linhai Co (600099) currently trades at ¥8.64, while our model-based Fair Value estimate is ¥1.90 — implying the stock looks roughly 78.0% overvalued today. We read business quality at 95/100 (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

Linhai Co.,Ltd. engages in the manufacture and sale of all-terrain vehicles, agricultural machinery, forest and fire-fighting machinery products, motorcycles, and other spare parts in China. The company also manufactures and sells product accessories. Linhai Co.,Ltd. was founded in 1956 and is based in Taizhou, China.

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Frequently asked questions

Is Linhai Co (600099) undervalued?
As of Jun 24, 2026, our model estimates a fair value of ¥1.90 versus a price of ¥8.64 — about −78% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 600099?
Our 21-model fair value for Linhai Co is ¥1.90 (as of Jun 24, 2026), built from audited fundamentals. The current price is ¥8.64.
What is the quality score of 600099?
Linhai Co has a Quality Score of 95/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.