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Hangzhou Jingye Intelligent Technology Co (688290) Fair Value & Analysis

Technology · CN · Market cap 6.3B CNY

Price¥68.00
Fair Value¥36.53
Upside-46.3%
Quality95/100
Evidence: Medium Range ¥27.40 – ¥45.66

Analysis

Hangzhou Jingye Intelligent Technology Co (688290) currently trades at ¥68.00, while our model-based Fair Value estimate is ¥36.53 — implying the stock looks roughly 46.3% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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: medium).

About the company

Hangzhou Jingye Intelligent Technology Co., Ltd. engages in the research and development of special robots and intelligent equipment to nuclear industry and military sectors in China. The company offers special-purpose robots, such as radiation-resistant articulated robot, explosion-proof AGV, embodied intelligence robots, and inspection UAV; special equipment, including nuclear medicine intelligent filling, intelligent support, and camouflage and protection equipment. It also offers digital systems, such as comprehensive information, production management, prognostics and health management, warehouse management, warehouse control, virtual training, robot control, and virtual training systema, as well as digital twin platform, interactive electronic manual, AI security agent, and AI all-in-one machine. Hangzhou Jingye Intelligent Technology Co., Ltd. was founded in 2015 and is headquartered in Hangzhou, 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.