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Wuxi Lihu Corporation (300694) Fair Value & Analysis

Industrials · CN · Market cap 2.3B CNY

Price¥9.94
Fair Value¥9.61
Upside-3.3%
Quality95/100
Evidence: High Range ¥7.21 – ¥12.01

Fair value as of: Jun 24, 2026

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Analysis

Wuxi Lihu Corporation (300694) currently trades at ¥9.94, while our model-based Fair Value estimate is ¥9.61 — implying the stock looks roughly 3.3% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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

Wuxi Lihu Corporation Limited. engages in the research and development, manufacture, and sale of turbocharger components in China and internationally. The company offers compressor and turbine casings; and turbine shell products. The company was founded in 1993 and is headquartered in Wuxi, China.

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Frequently asked questions

Is Wuxi Lihu Corporation (300694) undervalued?
As of Jun 24, 2026, our model estimates a fair value of ¥9.61 versus a price of ¥9.94 — about −3% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 300694?
Our 21-model fair value for Wuxi Lihu Corporation is ¥9.61 (as of Jun 24, 2026), built from audited fundamentals. The current price is ¥9.94.
What is the quality score of 300694?
Wuxi Lihu Corporation 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.