Sichuan Etrol Technologies Co (300370) Fair Value & Analysis
Technology · CN · Market cap 3.5B CNY
Analysis
Sichuan Etrol Technologies Co (300370) currently trades at ¥2.18, while our model-based Fair Value estimate is ¥0.8800 — implying the stock looks roughly 59.6% overvalued today. We read business quality at 85/100 (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
Sichuan Etrol Technologies Co., Ltd. engages in the research, development, manufacture, and sale of automation products in China and internationally. The company offers distributed control systems, remote terminal units, expansion I/O products, and instruments, as well as specialized industrial products for the oilfield, granary, environment protection, and chemical industries; and oil and gas products, such as ground devices, measurement while drilling, natural gas processing, and environmental protection, as well as integration of drilling and completion, and fract. It also provides intelligent industry products, including smart campus, intelligent environmental protection, smart granary, intelligent water, and others; digital oil and gas field, water and waste water, city gas, and coal bed methane automation business solutions; measurement while drilling, natural gas processing, city gas, oil production technology, environmental protection in oil and gas, and EPC solutions; and w…
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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.