Jianshe Industry Group (002265) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 20.3B CNY
Analysis
Jianshe Industry Group (002265) currently trades at ¥18.87, while our model-based Fair Value estimate is ¥4.96 — implying the stock looks roughly 73.7% 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: high).
About the company
Jianshe Industry Group (Yunnan) Co., Ltd., together with its subsidiaries, engages in the research, development, production, sale, and service of military and civilian products in China and internationally. The company offers military products, including full-caliber firearms and light weapons. It also provides civilian products, such as automotive powertrain, transmission, steering, and safety systems; new energy vehicle parts comprising mechanical steering gears, pedals, connecting rods, commercial vehicle steel pistons, and reducer gear blanks, as well as steering, intermediate, drive half, and motor shafts; precision forging products; other automotive parts; and civilian firearms. In addition, the company offers anti-terrorism and riot control products; training systems; optoelectronic information fusion equipment; titanium alloys; and powder metallurgy products. It exports its products. The company was formerly known as Yunnan Xiyi Industry Co., Ltd. and changed its name to Jia…
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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.