Shanghai Awinic Technology Co (688798) Fair Value & Analysis
Technology · CN · Market cap 15.0B CNY
Analysis
Shanghai Awinic Technology Co (688798) currently trades at ¥69.29, while our model-based Fair Value estimate is ¥26.19 — implying the stock looks roughly 62.2% 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: high).
About the company
Shanghai Awinic Technology Co.,Ltd. engages in the research, development, and sale of integrated circuit chips in China and internationally. It offers high-performance digital-analog hybrid chips, such as digital intelligent audio amplifier, tactile feedback chips, optical image stabilization chips, motor driver, sensing chips, sound and light synchronous breathing light driver chips, and other products. The company also provides power management chips, which includes backlight driver, breathing light driver, flash/infrared light driver, ToFLD driver, overvoltage protection, overcurrent protection, linear charging chips, high-power fast charging chips, switching power supply, LCD Bias, amoled Power, LDO, load switches, port protection switches, PD protocol chips, logic identification chips, MOSFET, and other products. In addition, it offers signal chain chip that includes antenna tuning switches, low noise amplifiers, low noise amplifiers; hall sensor chips, operational amplifiers, …
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.