Hebei Jianxin Chemical Co (300107) Fair Value & Analysis
Basic Materials · CN · Market cap 3.4B CNY
Fair value as of: Jun 24, 2026
Analysis
Hebei Jianxin Chemical Co (300107) currently trades at ¥6.06, while our model-based Fair Value estimate is ¥7.84 — implying the stock looks roughly 29.4% undervalued today. We read business quality at 95/100 (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: low) — always confirm before acting.
About the company
Hebei Jianxin Chemical Co., Ltd. researches and develops, manufactures, and sells dyes intermediates in China. The company offers metanilic acid, aniline-2, 5-disulfonic acid monosodium salt, 3- diethylaminophenol, m-aminophenol, sulfanilic acid, ODB-1, ODB-2, R-04, EBA, BBA, MMD, DPA, DBSP, BON, and n-butyl bromide. It also provides 3,3'-dinitrodiphenyl sulfone; 3,3'-diamino diphenyl sulphone; 4,4'-dichloro diphenyl sulphone; 4,4'-diamino diphenyl sulfone; and m-trihydroxybenzene. Its products are used as intermediates for dyes, medicine, fiber, pesticide, dyestuffs, and synthesizing health care products; OBA for paper; color formers for thermal and carbonless paper; and developer and sensitizer for thermal paper. The company's products are also used as alkylating agent, solvent, and extracting agent for rare elements and organic synthesis; materials for curing agents, epoxy resins, engineering plastics, dyestuffs, polysulfone amides, and other polymers; monomer for polysulfonamide…
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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.