Xi'an High Voltage Apparatus Research Institute Co (688334) Fair Value & Analysis
Industrials · CN · Market cap 6.0B CNY
Analysis
Xi'an High Voltage Apparatus Research Institute Co (688334) currently trades at ¥19.41, while our model-based Fair Value estimate is ¥16.50 — implying the stock looks roughly 15.0% overvalued today. We read business quality at 87/100 (high quality), in the Industrials 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
Xi'an High Voltage Apparatus Research Institute Co., Ltd., together with its subsidiaries, provides electrical inspection and testing services in China, Hong Kong, Macao, Taiwan, and internationally. The company offers professional technical services; type, performance, routine, and certificate tests; arbitration inspection; quality appraisement; and metrological verification of electrical equipment. It also provides product certification, greenhouse gas verification, green manufacturing evaluation, and electric power equipment inspection services; and technical research and consulting services for electrical equipment testing technology, key technologies of power transmission and transformation equipment, laboratory construction, electrical equipment fault analysis, specialized technical training in the field of power transmission and transformation, and technical and industry analysis. The company's services are used in high-voltage switches, insulators, surge arresters, capacitor…
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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.