Li Ning Company (LNNGY) Fair Value & Analysis
Consumer Cyclical · US · Market cap $5.3B
Analysis
Li Ning Company (LNNGY) currently trades at $49.14, while our model-based Fair Value estimate is $90.59 — implying the stock looks roughly 84.4% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Li Ning Company Limited, a sports brand company, engages in the research and development, design, manufacture, marketing, distribution, and retail of sporting goods in the People's Republic of China and internationally. The company offers sporting goods, including professional and leisure footwear, apparel, equipment, and accessories under the LI-NING brand. It also develops, manufactures, markets, distributes, and sells outdoor sports products under the AIGLE brand; table tennis products under the Double Happiness brand name; and badminton products under the Kason brand name. In addition, the company provides brand licensing, administrative, research and development, and property management services. Further, it operates conventional stores, flagship stores, China LI-NING stores, factory outlets, and multi-brand stores under the LI-NING brand. Li Ning Company Limited was founded in 1990 and is headquartered in Beijing, the People's Republic of China.
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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.