China Power Equipment, Inc (CPQQ) Fair Value & Analysis
Technology · US · Market cap $101K
Fair value as of: Jun 26, 2026
Analysis
China Power Equipment, Inc (CPQQ) currently trades at $0.0052, while our model-based Fair Value estimate is $0.0052 — implying the stock looks roughly 0.5% overvalued today. We read business quality at 89/100 (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
China Power Equipment, Inc., through its An Sen (Xi'an) Power Science & Technology Co., Ltd. subsidiary and its affiliated operating company, Xi'an Amorphous Alloy Zhongxi Transformer Co., Ltd., engages in the design, manufacture, and distribution of amorphous alloy transformer cores and amorphous alloy distribution transformers in the People's Republic of China. Its devices are used to step down voltage at the final phase of the distribution of electricity to consumers, businesses, and industries. The company offers its products to electricity generators and suppliers, suppliers of electrical equipment, and other electric power transformers manufacturers. China Power Equipment, Inc. was founded in 2004 and is headquartered in Jingyang, the People's Republic of China.
Open the full interactive analysis →
Similar stocks
Frequently asked questions
Is China Power Equipment, Inc (CPQQ) undervalued?
What is the fair value of CPQQ?
What is the quality score of CPQQ?
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.