Guangzhou Pearl River Piano Group (002678) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 7.0B CNY
Fair value as of: Jun 25, 2026
Analysis
Guangzhou Pearl River Piano Group (002678) currently trades at ¥4.63, while our model-based Fair Value estimate is ¥0.8900 — implying the stock looks roughly 80.8% overvalued today. We read business quality at 94/100 (high quality), in the Consumer Cyclical 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
Guangzhou Pearl River Piano Group Co.,Ltd engages in the research and development, manufacturing, sales and service of piano and digital musical instrument products in China and internationally. The company offers pianos, violins, digital pianos, strings, guitars, and wind instruments and folk music, as well as musical instrument accessories and music culture teaching materials. It is also involved in the art education and cultural media; radio, television, film and video recording production; house and venue rental and leasing; property management; and trading businesses. In addition, the company provides education consulting and professional technical services; and professional logistics of musical instruments, piano tuning, repair, rental, second-hand piano trading, piano playing teaching, and certification services. Further, it offers pianos under the Kayserburg, Ritmüller, Pearl River, and Pearl River Prodigy brand names. The company was founded in 1956 and is headquartered in …
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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.