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Wondershare Technology Group (300624) Fair Value & Analysis

Technology · CN · Market cap 10.5B CNY

Price¥58.30
Fair Value¥15.88
Upside-72.8%
Quality94/100
Evidence: Low Range ¥11.91 – ¥19.85

Analysis

Wondershare Technology Group (300624) currently trades at ¥58.30, while our model-based Fair Value estimate is ¥15.88 — implying the stock looks roughly 72.8% overvalued today. We read business quality at 94/100 (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

Wondershare Technology Group Co., Ltd. develops application software products in China and internationally. The company offers video creative software, such as Wondershare Filmora, Wondershare Virbo, Democreator, Wondershare Uniconverter, Beat.ly, Media.iostudio, SelfyzAI; Wondershare Filmstock, Miaoku video creative material platform and Aihua; AIGC applications, such as Bobo; drawing creative software, including EdrawMax, EdrawMind, and Edraw Project Management; document creative software, such as Wondershare PDF and PDFelement and PDFReader; and Recoverit, Data Manager, Wondershare Repairit, and Dr.Fone. The company's applications are used in desktop, mobile, and online terminal. The company was formerly known as Wondershare Technology Co.,Ltd and changed its name to Wondershare Technology Group Co., Ltd. in May 2019. The company was founded in 2003 and is based in Shenzhen, 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.