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GNCO Co (065060) Fair Value & Analysis

Consumer Cyclical · KR · Market cap 25.2B KRW

Price2,925 KRW
Fair Value1,670 KRW
Upside-42.9%
Quality80/100
Evidence: Low Range 1,252 KRW – 2,087 KRW

Fair value as of: Jun 24, 2026

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Analysis

GNCO Co (065060) currently trades at 2,925 KRW, while our model-based Fair Value estimate is 1,670 KRW — implying the stock looks roughly 42.9% overvalued today. We read business quality at 80/100 (high quality), in the Consumer Cyclical 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: low).

About the company

GNCO Co., Ltd. designs, manufactures, and sells apparel for men and women in South Korea and internationally. It offers its apparel through Thursday Island, T.I FOR MEN, ELOQ, LOUDMUT, PHENOM, and COVETBLAN brands. The company sells its products through its brand stores, as well as online. GNCO Co., Ltd. was founded in 1997 and is headquartered in Seoul, South Korea.

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Frequently asked questions

Is GNCO Co (065060) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 1,670 KRW versus a price of 2,925 KRW — about −43% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 065060?
Our 21-model fair value for GNCO Co is 1,670 KRW (as of Jun 24, 2026), built from audited fundamentals. The current price is 2,925 KRW.
What is the quality score of 065060?
GNCO Co has a Quality Score of 80/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.