CAS Corporation (016920) Fair Value & Analysis
Industrials · KR · Market cap 21.3B KRW
Fair value as of: Jun 25, 2026
Analysis
CAS Corporation (016920) currently trades at 912.00 KRW, while our model-based Fair Value estimate is 1,338 KRW — implying the stock looks roughly 46.7% undervalued today. We read business quality at 95/100 (high quality), in the Industrials 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
CAS Corporation manufactures and supplies electronic scales in South Korea. The company offers label printing scales, price computing scales, counter scales, basic scales, vacuum/container packing machines, sorters, hand wrappers, sealers, and printer supplies for retail clients. It also provides industrial scales, check weighers/metal detectors, liquid filling machines, auto packaging machines, indicators, weighing components, load cells, printers, and strain gages for industrial clients. In addition, the company offers micro weighing, electronic balance, weighing equipment, and thermal imaging cameras for laboratories; and PC software solutions for retail and industrial clients. Its products are also used in product inspection and logistics applications. The company also exports its products to approximately 100 countries worldwide. CAS Corporation was founded in 1983 and is headquartered in Yangju-si, South Korea.
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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.