Xinjiang Xuefeng Sci-Tech(Group)Co.,Ltd, (603227) Fair Value & Analysis
Basic Materials · CN · Market cap 8.6B CNY
Analysis
Xinjiang Xuefeng Sci-Tech(Group)Co.,Ltd, (603227) currently trades at ¥7.75, while our model-based Fair Value estimate is ¥7.99 — implying the stock looks roughly 3.1% undervalued today. We read business quality at 80/100 (high quality), in the Basic Materials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Xinjiang Xuefeng Sci-Tech(Group)Co.,Ltd, together with its subsidiaries, produces and sells civil explosives. The company's products include expanded ammonium nitrate explosives, emulsion explosives, industrial electronic detonators, detonating cord detonators, plastic detonating cords, industrial detonating cords, and on-site mixed explosives; and industrial explosives, detonators, and cables. It also engages in the production and sale of chemical products, such as liquid ammonia, urea, nitric acid, ammonium nitrate, nitro compound fertilizer, melamine, UAN liquid fertilizer, and dilute ammonia water; fertilizers, and liquefied natural gas; and blasting services, hazardous materials transportation; transportation of dangerous goods and natural gas pipeline transportation business. In addition, the company provides engineering blasting services comprising drilling and transportation; and professional services, such as blasting scheme design, blasting engineering supervision, and pla…
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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.