Daewon Kang Up Co (000430) Fair Value & Analysis
Consumer Cyclical · KR · Market cap 314B KRW
Fair value as of: Jun 25, 2026
Analysis
Daewon Kang Up Co (000430) currently trades at 3,940 KRW, while our model-based Fair Value estimate is 9,987 KRW — implying the stock looks roughly 153.5% undervalued today. We read business quality at 80/100 (high quality), in the Consumer Cyclical 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
Daewon Kang Up Co., Ltd. develops, produces, and sells suspension springs and seats in South Korea and internationally. The company offers round steel, flat steel, material processing, and piping spring materials; and suspension springs, such as coil and air springs, stabilizer and torsion bars, and leaf springs. It also provides precision springs, such as engine valve springs, auto clutch return springs, curved springs, various wire springs, and springs for IT parts; industrial springs, including die and disc springs, coil inserts, and tension bar connections; manufacturing equipment for coil and leaf springs, test equipment for springs, and equipment for materials processing; and engineering services. In addition, the company offers seats for automobiles, railway vehicles, high-speed trains, and heavy equipment; and adjusters, recliners, pumping devices, active headrests, magnesium frames, and seat assemblies. The company was founded in 1946 and is headquartered in Cheonan-si, Sou…
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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.