Funshine Culture Group (300860) Fair Value & Analysis
Communication Services · CN · Market cap 4.9B CNY
Analysis
Funshine Culture Group (300860) currently trades at ¥26.10, while our model-based Fair Value estimate is ¥15.33 — implying the stock looks roughly 41.3% overvalued today. We read business quality at 94/100 (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
Funshine Culture Group Co.,Ltd. engages in the design and production services for cultural performances in China. The company is involved in the cultural celebrations or commemorative events, TV programs, performances, TV cultural evenings, corporate brand cultural activities, and opening and closing ceremonies of sports events; and tourism performances, evening tours, theme parks, cultural tourism complexes, and industrial parks cultural tourism project services. It also engages in scenic lighting design and production, landscape and architectural artistic lighting design, and landscape and architectural light show; content production, as well as musicals, artist management, music entertainment, and short dramas. The company was formerly known as Beijing Funshine Culture Media Co., Ltd. and changed its name to Funshine Culture Group Co.,Ltd. in March 2023. Funshine Culture Group Co.,Ltd. was founded in 2002 and is headquartered in Beijing, 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.