Fanli Digital Technology Co (600228) Fair Value & Analysis
Communication Services · CN · Market cap 3.1B CNY
Fair value as of: Jun 24, 2026
Analysis
Fanli Digital Technology Co (600228) currently trades at ¥8.92, while our model-based Fair Value estimate is ¥2.85 — implying the stock looks roughly 68.0% overvalued today. We read business quality at 95/100 (high quality), in the Communication Services 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
Fanli Digital Technology Co.,Ltd provides internet and related services in China and internationally. The company offers online shopping guide services; advertising and promotion services, including strategy planning, content creation, ad placement, e-commerce traffic generation, data monitoring, and performance optimization; related platform services; services to small and medium-sized businesses that require order data and advertising strategy management; and technical services, such as order feedback and delivery optimization. It also provides online shopping guide platforms comprising cashback apps, cashback websites, mini-programs, and official accounts; and operates Fanli.com and its Fanli APP, a shopping guide platform. In addition, the company offers big data analytics and personalized recommendation technology, mobile application development technology, shopping guide service technology, and advertising placement services. The company was formerly known as Fanli Inc and cha…
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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.