Chunghwa Precision Test Tech. Co (6510) Fair Value & Analysis
Technology · TW · Market cap 108B TWD
Fair value as of: Jun 24, 2026
Analysis
Chunghwa Precision Test Tech. Co (6510) currently trades at 3,665 TWD, while our model-based Fair Value estimate is 608.24 TWD — implying the stock looks roughly 83.4% 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
Chunghwa Precision Test Tech. Co., Ltd., together with its subsidiaries, engages in the testing of semiconductor components in Taiwan, Republic of China, and internationally. The company is involved in the design, processing, assembly, production, and sale of printed circuit boards and related consultation services; and markets electronic products. It also offers probe cards for use in wafer testing, as well as to examine wafer yields after production; load boards that are interface for yield testing after IC packaging during the back-end process of semiconductor manufacturing; and probing needles, which transmit signals to the substrate and the probe cards. In addition, the company maintains and sells electronic parts and machinery processed products; invests in, retails, and wholesales electronic materials; investment activities; manufacture and retail of intellectual equipment, software and hardware system combination of intellectual factory and technology consulting services. Ch…
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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.