SFA Engineering Corporation (056190) Fair Value & Analysis
Industrials · KR · Market cap 762B KRW
Fair value as of: Jun 24, 2026
Analysis
SFA Engineering Corporation (056190) currently trades at 23,850 KRW, while our model-based Fair Value estimate is 38,894 KRW — implying the stock looks roughly 63.1% undervalued today. We read business quality at 80/100 (high quality), in the Industrials 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: medium) — always confirm before acting.
About the company
SFA Engineering Corporation manufactures and sells display equipment, logistics systems, secondary battery, semiconductor equipment, material handling, and special business equipment in South Korea and internationally. The company offers smart factory, that includes NEO AI solution and NEO platform solution; semiconductor equipment, such as clean logistics system and test equipment, as well as N2 stocker, OHT, AGV, inter-layer high-speed lifter, clean module conveyor, and application test; and module equipment; and front-end equipment. It also provides special business that includes aerospace and nuclear fusion; secondary battery, such as logistics system, process and precision equipment, and inspect and measure equipment; consulting, design, simulation, production, installation, test-operation, and after-sales services; distribution centers, automated warehouse system, and factory automated system. The company was founded in 1998 and is headquartered in Hwaseong-si, South Korea.
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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.