Suzhou Sushi Testing Group (300416) Fair Value & Analysis
Technology · CN · Market cap 9.9B CNY
Analysis
Suzhou Sushi Testing Group (300416) currently trades at ¥19.44, while our model-based Fair Value estimate is ¥10.12 — implying the stock looks roughly 47.9% overvalued today. We read business quality at 88/100 (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
Suzhou Sushi Testing Group Co.,Ltd. provides industrial product environmental and reliability test verification, and analysis service solutions in China. The company offers mechanical environmental test equipment, including vibration testing systems, and vibration centrifugal integrated testing systems; and climate and environmental test equipment, such as high and low temperature, temperature shock, walk-in, vehicle, solar radiation simulation, salt spray, and rain test systems, as well as dust storm system, and accelerated life test and stress screening system. It also provides environmental test equipment, including integrated vibration-temperature, integrated vibration-temperature-humidity, integrated vibration-temperature-humidity-low pressure environmental test systems, multi-functional vehicle cabins, and electromagnetic wave absorbable environmental reliability test systems; thermal vacuum testing systems, thermal balance testing systems, thermal optical testing systems, and…
Open the full interactive analysis →
Similar stocks
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.