Jiangsu Gian Technology Co (300709) Fair Value & Analysis
Industrials · CN · Market cap 11.2B CNY
Analysis
Jiangsu Gian Technology Co (300709) currently trades at ¥65.36, while our model-based Fair Value estimate is ¥22.08 — implying the stock looks roughly 66.2% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Jiangsu Gian Technology Co., Ltd. manufactures and sells metal injection molding products in China and internationally. The company offers metal injection molding parts, such as laptop fan, watch case, auto parts, camera module, and medical equipment; plastic parts, including watch frame, socket, socket head, headphone shell, gear, industrial meter box shell, vacuum cleaner shell, and COVID-19 test paper shell; and CNC parts comprising laptop c parts, headphone parts, magnetic isolation baffle, and shading plate. It also provides car camera, aluminum drawn, automotive decorative ring, and water-cooling interface products; precision injection mold products; and 3D printing products. The company's products are used in consumer electronics, medical devices, automotive, servers, 5G base stations, edge computing, new energy, energy storage, beauty and personal care, and smart home applications. Jiangsu Gian Technology Co., Ltd. was founded in 2004 and is headquartered in Changzhou, China.
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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.