Jiangxi Black Cat Carbon Black Inc (002068) Fair Value & Analysis
Basic Materials · CN · Market cap 7.4B CNY
Analysis
Jiangxi Black Cat Carbon Black Inc (002068) currently trades at ¥11.50, while our model-based Fair Value estimate is ¥9.90 — implying the stock looks roughly 13.9% overvalued today. We read business quality at 93/100 (high quality), in the Basic Materials 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
Jiangxi Black Cat Carbon Black Inc.,Ltd, together with its subsidiaries, manufactures and sells carbon black products in China. It operates through Carbon Black, Tar Refining, and Other segments. The company offers carbon black for rubber, special carbon black, green carbon black, precipitated and fumed silica, and chemical products; and tar refining and white carbon black products. It is also involved in power generation, transmission, distribution, and supply; energy conservation and environmental protection consultation, renovation, and installation; research, development, production, testing, and consulting on carbon composites and nanomaterials; and carbon black exhaust gas waste heat power generation. In addition, the company offers carbon black oil; chemical raw materials; hazardous chemicals; consulting services; graphite products; pigments; batteries; fiber, composite, and synthetic materials; and rubber products. It exports its products. The company was founded in 1994 and…
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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.