TAEYANG Corporation (053620) Fair Value & Analysis
Basic Materials · KR · Market cap 54.1B KRW
Fair value as of: Jun 25, 2026
Analysis
TAEYANG Corporation (053620) currently trades at 6,400 KRW, while our model-based Fair Value estimate is 18,311 KRW — implying the stock looks roughly 186.1% undervalued today. We read business quality at 95/100 (high quality), in the Basic Materials 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: high) — always confirm before acting.
About the company
TAEYANG Corporation provides portable butane gas cartridges, stoves, and gas filling and aerosol products in South Korea. It offers portable fuel products, including portable butane gas, portable lighter gas refills, portable camping gas, portable fuel, sun fuel gas stoves, and mini sun fuel gas cartridges; LPG and pure butane gas under the ALPS name for use in mountain climbing; and lighter gas, lighter oil, and portable gas products. The company also provides cosmetics, such as hair mousse, hair sprays, skin lotions, shaving foams, deodorants, etc.; medical supplies comprising insecticides and insect repellents; air fresheners and iron glue; and industrial supplies consisting of adhesives, polyurethane, carburetor, printer cleaner, rust, frost removers, etc. In addition, it offers household items, including zipper bags, vinyl gloves, roll bags, and paper cups. The company was formerly known as Taeyang Industrial Co., Ltd. and changed its name to TAEYANG Corporation in September 20…
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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.