Suzhou Agioe Technologies Co (688450) Fair Value & Analysis
Industrials · CN · Market cap 2.7B CNY
Fair value as of: Jun 24, 2026
Analysis
Suzhou Agioe Technologies Co (688450) currently trades at ¥52.94, while our model-based Fair Value estimate is ¥63.99 — implying the stock looks roughly 20.9% undervalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Suzhou Agioe Technologies Co., Ltd. engages in the research, development, manufacture, and sale of optical fiber sensing products, IoT smart devices, and asset digital AIoT cloud platforms in China. The company offers fiber optic sensing products, linear fiber optic temperature fire detectors, point fiber optic temperature sensors, temperature measuring electrical fire detectors, optical fiber temperature sensors, optical fiber acoustic sensors, and optical fiber temperature strain sensors. It also provides edge gateways and terminals comprising online cable partial discharge monitoring systems, cable ampacity monitoring and evaluation systems, cable sheath circulation monitoring systems, and high-voltage cable distributed fault online location systems; and area control units, programmable edge agents, and intelligent edge agents, as well as intelligent inspection robots. In addition, the company offers asset lean management operation and maintenance, monitoring alarm operation and …
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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.