HanWool Semiconductor, Inc (320000) Fair Value & Analysis
Technology · KR · Market cap 76.2B KRW
Fair value as of: Jun 24, 2026
Analysis
HanWool Semiconductor, Inc (320000) currently trades at 20,450 KRW, while our model-based Fair Value estimate is 22,173 KRW — implying the stock looks roughly 8.4% undervalued today. We read business quality at 81/100 (high quality), in the Technology 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: low) — always confirm before acting.
About the company
HanWool Semiconductor, Inc. provides x-ray, microchip, film and FPD, and artificial intelligence products. It offers WVS-6CX.AI, a device that inspects the external condition of microchips at high speed using proprietary algorithms and AI; WVS-RP3000, a device that inspects the condition of micro chips and reel tapes packed in reels at high speed; WVS-AM100CIB, a device that measures the electrical characteristics of microchips; WVS-MT300, a device that precisely mounts microchips on a PCB and board using pick and placement; WVF-CUxxx Series, a device that automatically inspects copper foil films; WXI-CT160, a device that inspect the product for defects through non-destructive inspection using nano focus X-ray; WXI-CT130, a device that inspect the product for defects through non-destructive inspection using micro focus X-ray; COF Insp. System, a COF PKG visual inspection system; WVS-AKKON, a device that inspects the conductive particles of the LCD and OLED Panel; and WVS-BONDER, a d…
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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.