Chia Chang Co (4942) Fair Value & Analysis
Technology · TW · Market cap 5.3B TWD
Fair value as of: Jun 24, 2026
Analysis
Chia Chang Co (4942) currently trades at 37.80 TWD, while our model-based Fair Value estimate is 51.26 TWD — implying the stock looks roughly 35.6% undervalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
Chia Chang Co., Ltd engages in manufacturing, processing, and sale of optoelectronic and information electronic components, molds, and precision instruments in Taiwan, Japan, and South Korea. The company offers 1U server, semiconductor equipment component, wireless access point, wireless access point chassis, wireless access point reflector, and chassis of camera; medical device metal fabrication, automotive display and consumer electronic products; and custom metal fabrication products. Its products have applications in optoelectronics, IT, and communication industries, including both internal and external metal processing parts, and related precision mold design and manufacturing. The company serves panel manufacturers, backlight module suppliers, and IT OEM/ODM companies. It is also involved in the import and export of related products. Chia Chang Co., Ltd was incorporated in 1985 and is headquartered in Taoyuan City, Taiwan.
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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.