KG Chemical Corporation (001390) Fair Value & Analysis
Industrials · KR · Market cap 312B KRW
Analysis
KG Chemical Corporation (001390) currently trades at 4,065 KRW, while our model-based Fair Value estimate is 3,896 KRW — implying the stock looks roughly 4.2% overvalued today. We read business quality at 80/100 (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: medium).
About the company
KG Chemical Corporation manufactures and supplies fertilizers and concrete compound ingredients products in South Korea and internationally. The company offers mineral and organic fertilizers; and eco-friendly substances for various soil types to farmers. It also provides chemical materials, including water reducing agents, preservatives, compounds, and dispersants, as well as naphthalene dispersants for use in pigment, rubber, and latex manufacturing industries. In addition, the company offers POWERCON, a material for ready mix, under water, and self-compacting concrete; concrete secondary products, including precast concrete and concrete piles; fly ash, blast furnace slag, and lightweight concrete; and gypsum boards. Further, it provides DYWELL, a dispersant of liquid/power for dye stuff, pigments, latex, and rubber; and POWERFLOW for use in as a water reducing effect, improving concrete strength, and reduction agent of bleeding and materials separation. Additionally, the company …
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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.