Winner Technology Co (300609) Fair Value & Analysis
Technology · CN · Market cap 3.9B CNY
Analysis
Winner Technology Co (300609) currently trades at ¥33.03, while our model-based Fair Value estimate is ¥23.43 — implying the stock looks roughly 29.1% overvalued today. We read business quality at 90/100 (high quality), in the Technology 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
Winner Technology Co., Inc. provides artificial intelligence (AI) and big data application solutions. The company offers digital marketplace solutions comprising AI customer flow solutions, big data service solutions for shopping malls, AI-powered indoor navigation systems, and metaverse construction operations programs; and digital store solutions, such as Smart Stores AI customer flow solutions, big data service solutions for brand stores, smart store AI remote inspection solutions, and intelligence store AI business display solutions. It also provides digital government solutions, including city/ municipalities commerce big data platforms, urban/city public space analysis platforms, government hotline data governance and analysis platforms, government online-offline Shanghai, digital comprehensive research and analysis platform, and platform for integrated governance and application development for government data; and digital political solutions consisting of smart court systems…
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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.