Cipherlab Co (6160) Fair Value & Analysis
Industrials · TW · Market cap 1.1B TWD
Fair value as of: Jun 25, 2026
Analysis
Cipherlab Co (6160) currently trades at 16.55 TWD, while our model-based Fair Value estimate is 5.65 TWD — implying the stock looks roughly 65.9% overvalued today. We read business quality at 95/100 (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: low).
About the company
Cipherlab Co.,Ltd. designs, manufactures, markets, and sells automatic identification and data capture products under the CipherLab brand name in Taiwan, Mainland China, and the United States. The company offers windows/android and proprietary mobile computers; general purpose, business durable, and pocket sized barcode scanners; HF and UHF radio frequency identification readers; and software solutions comprising deployment and development tools, device management, terminal emulators, enterprise browser, optical character recognition, programming, and scanner software. It also provides computer-related products, such as peripheral equipment, electronic cash registers, magnetic card readers, and electronic measuring instruments; services for skincare products; and import and export trade services, as well as acts as domestic and foreign manufacturers' agents for distribution. The company serves warehouse, transportation and logistics, field mobility, retail, manufacturing, and health…
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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.