Jonjee Hi-Tech Industrial and Commercial Holding (600872) Fair Value & Analysis
Consumer Defensive · CN · Market cap 13.8B CNY
Analysis
Jonjee Hi-Tech Industrial and Commercial Holding (600872) currently trades at ¥17.43, while our model-based Fair Value estimate is ¥13.44 — implying the stock looks roughly 22.9% overvalued today. We read business quality at 92/100 (high quality), in the Consumer Defensive 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: high).
About the company
Jonjee Hi-Tech Industrial and Commercial Holding Co.,Ltd produces and sells seasoning products in China and internationally. The company offers soy sauce, chicken powder, oyster sauce, sauce, cooking wine, vinegar, edible oil, fermented bean curd, sauces, monosodium glutamate, and other seasoning products. It also provides property services, gardening, and housing brokerage services. In addition, the company engages in the property management, property leasing, property services, and various professional consulting services, as well as provides cleaning and greening maintenance, security, property maintenance and custody, and consulting services. Further, it involved in manufacturing and sale of precision forgings and machined products for automobiles and motorcycles. The company was formerly known as Zhongshan Torch Hi-tech Industrial & Commercial Co., Ltd. and changed its name to Jonjee Hi-Tech Industrial and Commercial Holding Co.,Ltd in February 2001. Jonjee Hi-Tech Industrial a…
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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.