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S-Prism Co (288620) Fair Value & Analysis

Industrials · KR · Market cap 48.6B KRW

Price5,560 KRW
Fair Value3,951 KRW
Upside-28.9%
Quality95/100
Evidence: Low Range 2,607 KRW – 4,938 KRW

Fair value as of: Jun 25, 2026

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Analysis

S-Prism Co (288620) currently trades at 5,560 KRW, while our model-based Fair Value estimate is 3,951 KRW — implying the stock looks roughly 28.9% overvalued today. We read business quality at 95/100 (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: low).

About the company

S-Prism Co., Ltd. manufactures and sells hydrogen fuel cell systems in South Korea. The company's products are used in buildings, transport, and power generation businesses. The company was formerly known as S-Fuelcell co., Ltd. and change its name to S-Prism Co., Ltd. in April 2026. S-Prism Co., Ltd. was founded in 1989 and is headquartered in Seoul, South Korea.

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Frequently asked questions

Is S-Prism Co (288620) undervalued?
As of Jun 25, 2026, our model estimates a fair value of 3,951 KRW versus a price of 5,560 KRW — about −29% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 288620?
Our 21-model fair value for S-Prism Co is 3,951 KRW (as of Jun 25, 2026), built from audited fundamentals. The current price is 5,560 KRW.
What is the quality score of 288620?
S-Prism Co has a Quality Score of 95/100, 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.