HangZhou Everfine Photo-e-info Co (300306) Fair Value & Analysis
Technology · CN · Market cap 2.8B CNY
Fair value as of: Jun 24, 2026
Analysis
HangZhou Everfine Photo-e-info Co (300306) currently trades at ¥10.25, while our model-based Fair Value estimate is ¥5.37 — implying the stock looks roughly 47.6% overvalued today. We read business quality at 93/100 (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
HangZhou Everfine Photo-e-info Co., Ltd. provides photoelectric, biometric, Infrared, UV, EMC, and DNA diagnosis products in China. The company offers lighting measurement products comprising of spectroradiometer integrating sphere system, goniophotometer, aging lifetime, thermal resistance test, optical radiation safety, blue light hazard test, irradiance colorimeter, flicker, glare test, and plant lighting test. It also offers ultraviolet radiation test and infrared radiation test; light distribution test, on-line and ambient lighting distribution test, vehicle projection light test, head up display test, and vehicle display and CMS test products. In addition, the company provides universal luminance meter series, mobile phone display test, TV display, OLED display, projection display, and AR/VR display test products. Further, it offers spectrocolorimeter, haze meter, visual color assessment, glossmeter, and BSDF spatial scattering distribution products; power analyzer, digital po…
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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.