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VM Inc (089970) Fair Value & Analysis

Technology · KR · Market cap 1.6T KRW

Price93,300 KRW
Fair Value13,237 KRW
Upside-85.8%
Quality95/100
Evidence: High Range 10,353 KRW – 24,232 KRW

Fair value as of: Jun 24, 2026

Analysis

VM Inc (089970) currently trades at 93,300 KRW, while our model-based Fair Value estimate is 13,237 KRW — implying the stock looks roughly 85.8% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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: high).

About the company

VM Inc. manufactures and sells dry etcher systems that are used for semiconductor production process in South Korea and internationally. The company offers poly, metal, and oxide etcher systems. The company was formerly known as Adaptive Plasma Technology Corp. and changed its name to VM Inc. in March 2024. VM Inc. was founded in 2002 and is based in Icheon-si, South Korea.

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Frequently asked questions

Is VM Inc (089970) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 13,237 KRW versus a price of 93,300 KRW — about −86% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 089970?
Our 21-model fair value for VM Inc is 13,237 KRW (as of Jun 24, 2026), built from audited fundamentals. The current price is 93,300 KRW.
What is the quality score of 089970?
VM Inc 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.